@inproceedings{ZhaoWangLW11,
  author    = {Yuhai Zhao and Guoren Wang and Yuan Li and Zhanghui Wang},
  title     = {Finding Novel Diagnostic Gene Patterns based on Interesting
               Non-redundant Contrast Sequence Rules},
  booktitle = {IEEE International Conference on Data Mining (ICDM)},
  year      = {2011},
  pages     = {972-981}
}

@ARTICLE{YoonBoostingAR11,
  AUTHOR =       {Yongwook Yoon and Gary Geunbae Lee},
  TITLE =        {Subcellular Localization Prediction through Boosting Association Rules },
  JOURNAL =      {IEEE/ACM transactions on computational biology and bioinformatics},
  YEAR =         {2011},
  abstract =     {%GD note: looks like CAEP-style classification.
  Computational methods for predicting protein subcellular localization have used various types of features, including N-terminal sorting signals, amino acid compositions, and text annotations from protein databases. Our approach does not use biological knowledge such as the sorting signals or homologues, but use just protein sequence information. The method divides a protein sequence into short $k$-mer sequence fragments which can be mapped to word features in document classification. A large number of class association rules are mined from the protein sequence examples that range from the N-terminus to the C-terminus. Then, a boosting algorithm is applied to those rules to build up a final classifier. Experimental results using benchmark datasets show our method is excellent in terms of both the classification performance and the test coverage. The result also implies that the $k$-mer sequence features which determine subcellular locations do not necessarily exist in specific positions of a protein sequence. Online prediction service implementing our method is available at http://isoft.postech.ac.kr/research/BCAR/subcell.},
}

@article{ipl/DingWQ10visualwordweighing,
  author    = {Guiguang Ding and
               Jianmin Wang and
               Kai Qin},
  title     = {A visual word weighting scheme based on emerging itemsets
               for video annotation},
  journal   = {Inf. Process. Lett.},
  volume    = {110},
  number    = {16},
  year      = {2010},
  pages     = {692-696},
  ee        = {http://dx.doi.org/10.1016/j.ipl.2010.05.027},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  abstract={The method based on Bag-of-visual-Words (BoW) deriving from local keypoints has recently appeared promising
  for video annotation. Visual word weighting scheme has critical impact to the performance of BoW method. In this paper,
  we propose a new visual word weighting scheme which is referred as emerging patterns weighting (EP-weighting).
  The EP-weighting scheme can efficiently capture the co-occurrence relationships of visual words and improve the
  effectiveness of video annotation. The proposed scheme firstly finds emerging patterns (EPs) of visual keywords in
  training dataset. And then an adaptive weighting assignment is performed for each visual word according to EPs. The
  adjusted BoW features are used to train classifiers for video annotation. A systematic performance study on TRECVID corpus
  containing 20 semantic concepts shows that the proposed scheme is more effective than other popular existing weighting schemes.}
}

@inproceedings{icannga/DominikWW07,
  author    = {Andrzej Dominik and
               Zbigniew Walczak and
               Jacek Wojciechowski},
  title     = {Classifying Chemical Compounds Using Contrast and Common
               Patterns},
  booktitle = {8th International
               Conference on Adaptive and Natural Computing Algorithms (ICANNGA)},
  year      = {2007},
  pages     = {772-781},
  ee        = {http://dx.doi.org/10.1007/978-3-540-71618-1_86},
  crossref  = {DBLP:conf/icannga/2007-1},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{jsw/ChenC11,
  author    = {Xiaoyun Chen and
               Jinhua Chen},
  title     = {Emerging Patterns and Classification Algorithms for {DNA}
               Sequence},
  journal   = {Journal of Software},
  volume    = {6},
  number    = {6},
  year      = {2011},
  pages     = {985-992},
  ee        = {http://dx.doi.org/10.4304/jsw.6.6.985-992},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{mms/HFLi11,
  author    = {Hua-Fu Li},
  title     = {{MEMSA}: mining emerging melody structures from music query
               data},
  journal   = {Multimedia Syst.},
  volume    = {17},
  number    = {3},
  year      = {2011},
  pages     = {237-245},
  ee        = {http://dx.doi.org/10.1007/s00530-010-0226-5},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{ichit/PiaoLSYR11,
  author    = {Minghao Piao and
               Jong Bum Lee and
               Ho-Sun Shon and
               Unil Yun and
               Keun Ho Ryu},
  title     = {Enumeration Tree Based Emerging Patterns Mining by Using
               Two Different Supports},
  booktitle = {5th International
               Conference on Convergence and Hybrid Information Technology
               (ICHIT)},
  year      = {2011},
  pages     = {708-715},
  ee        = {http://dx.doi.org/10.1007/978-3-642-24082-9_86},
  crossref  = {DBLP:conf/ichit/2011},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{icdm/YuWDW11,
  author    = {Kui Yu and
               Xindong Wu and
               Wei Ding and Hao Wang},
  title     = {Causal Associative Classification},
  booktitle = {IEEE International Conference on Data
               Mining (ICDM)},
  year      = {2011}
}

@inproceedings{ismis/PoezevaraCC09,
  author    = {Guillaume Poezevara and
               Bertrand Cuissart and
               Bruno Cr{\'e}milleux},
  title     = {Discovering Emerging Graph Patterns from Chemicals},
  booktitle = {International Symposium on Foundations of Intelligent Systems
               (ISMIS)},
  year      = {2009},
  pages     = {45-55},
  ee        = {http://dx.doi.org/10.1007/978-3-642-04125-9_8},
  crossref  = {DBLP:conf/ismis/2009},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{jiis/PoezevaraCC11,
  author    = {Guillaume Poezevara and
               Bertrand Cuissart and
               Bruno Cr{\'e}milleux},
  title     = {Extracting and summarizing the frequent emerging graph patterns
               from a dataset of graphs},
  journal   = {J. Intell. Inf. Syst.},
  volume    = {37},
  number    = {3},
  year      = {2011},
  pages     = {333-353},
  ee        = {http://dx.doi.org/10.1007/s10844-011-0168-1},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{icdm/JinBL09,
  author    = {Ruoming Jin and
               Yuri Breitbart and
               Rong Li},
  title     = {A Tree-Based Framework for Difference Summarization},
  booktitle = {IEEE International Conference on Data
               Mining (ICDM)},
  year      = {2009},
  pages     = {209-218},
  ee        = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2009.68},
  crossref  = {DBLP:conf/icdm/2009},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{bibe/TzanisKV08,
  author    = {George Tzanis and
               Ioannis Kavakiotis and
               Ioannis P. Vlahavas},
  title     = {Polyadenylation site prediction using interesting emerging
               patterns},
  booktitle = {IEEE International Conference on
               Bioinformatics and Bioengineering (BIBE)},
  year      = {2008},
  pages     = {1-7},
  ee        = {http://dx.doi.org/10.1109/BIBE.2008.4696711},
  crossref  = {DBLP:conf/bibe/2008},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{eswa/TzanisKV11,
  author    = {George Tzanis and
               Ioannis Kavakiotis and
               Ioannis P. Vlahavas},
  title     = {{PolyA-iEP}: A data mining method for the effective prediction
               of polyadenylation sites},
  journal   = {Expert Syst. Appl.},
  volume    = {38},
  number    = {10},
  year      = {2011},
  pages     = {12398-12408},
  ee        = {http://dx.doi.org/10.1016/j.eswa.2011.04.019},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{ideal/MuyebaKWK11,
  author    = {Maybin K. Muyeba and
               Muhammad S. Khan and
               Spits Warnars and
               John A. Keane},
  title     = {A Framework to Mine High-Level Emerging Patterns by Attribute-Oriented
               Induction},
  booktitle = {International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)},
  year      = {2011},
  pages     = {170-177},
  ee        = {http://dx.doi.org/10.1007/978-3-642-23878-9_21},
  crossref  = {DBLP:conf/ideal/2011},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{fskd/SaeedLKCR09Ryu,
  author    = {Khalid E. K. Saeed and
               Heon Gyu Lee and
               Wun-Jae Kim and
               Eun Jong Cha and
               Keun Ho Ryu},
  title     = {Using Emerging Subsequence in Classifying Protein Structural
               Class},
  booktitle = {International Conference on Fuzzy Systems
               and Knowledge Discovery (FSKD)},
  year      = {2009},
  pages     = {349-353},
  ee        = {http://doi.ieeecomputersociety.org/10.1109/FSKD.2009.752},
  crossref  = {DBLP:conf/fskd/2009},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{fskd/ParkLSSR09Ryu,
  author    = {Jin Hyoung Park and
               Heon Gyu Lee and
               Gyoyong Sohn and
               Jin-Ho Shin and
               Keun Ho Ryu},
  title     = {Emerging Pattern Based Classification for Automated Non-safe
               Power Line Detection},
  booktitle = {International Conference on Fuzzy Systems
               and Knowledge Discovery (FSKD)},
  year      = {2009},
  pages     = {169-173},
  ee        = {http://doi.ieeecomputersociety.org/10.1109/FSKD.2009.769},
  crossref  = {DBLP:conf/fskd/2009},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@inproceedings{fskd/PiaoLSPR09Ryu,
  author    = {Minghao Piao and
               Heon Gyu Lee and
               Gyoyong Sohn and
               Gouchol Pok and
               Keun Ho Ryu},
  title     = {Emerging Patterns Based Methodology for Prediction of Patients
               with Myocardial Ischemia},
  booktitle = {International Conference on Fuzzy Systems
               and Knowledge Discovery (FSKD)},
  year      = {2009},
  pages     = {174-178},
  ee        = {http://doi.ieeecomputersociety.org/10.1109/FSKD.2009.638},
  crossref  = {DBLP:conf/fskd/2009},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{widm/Boettcher11,
  author    = {Mirko B{\"o}ttcher},
  title     = {Contrast and change mining},
  journal   = {Wiley Interdisc. Rew.: Data Mining and Knowledge Discovery},
  volume    = {1},
  number    = {3},
  year      = {2011},
  pages     = {215-230},
  ee        = {http://dx.doi.org/10.1002/widm.27},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{pakdd/SteinbachYFK11,
  author    = {Michael Steinbach and
               Haoyu Yu and
               Gang Fang and
               Vipin Kumar},
  title     = {Using Constraints to Generate and Explore Higher Order Discriminative
               Patterns},
  booktitle = {Advances in Knowledge Discovery and Data Mining - 15th Pacific-Asia
               Conference (PAKDD)},
  year      = {2011},
  pages     = {338-350},
  ee        = {http://dx.doi.org/10.1007/978-3-642-20841-6_28},
  crossref  = {DBLP:conf/pakdd/2011-1},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@inproceedings{rsctc/TerleckiW08topkJEP,
  author    = {Pawel Terlecki and
               Krzysztof Walczak},
  title     = {Efficient Discovery of Top-K Minimal Jumping Emerging Patterns},
  booktitle = {6th International
               Conference on Rough Sets and Current Trends in Computing},
  year      = {2008},
  pages     = {438-447},
  ee        = {http://dx.doi.org/10.1007/978-3-540-88425-5_45},
  crossref  = {DBLP:conf/rsctc/2008},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@inproceedings{ausai/QianBL06,
  author    = {Xiaoyuan Qian and
               James Bailey and
               Christopher Leckie},
  title     = {Mining Generalised Emerging Patterns},
  booktitle = {Australian Conference on Artificial Intelligence},
  year      = {2006},
  pages     = {295-304},
  ee        = {http://dx.doi.org/10.1007/11941439_33},
  crossref  = {DBLP:conf/ausai/2006},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{kdd/MorchenDFEWB08,
  author    = {Fabian M{\"o}rchen and
               Math{\"a}us Dejori and
               Dmitriy Fradkin and
               Julien Etienne and
               Bernd Wachmann and
               Markus Bundschus},
  title     = {Anticipating annotations and emerging trends in biomedical
               literature},
  booktitle = {ACM SIGKDD International Conference
               on Knowledge Discovery and Data Mining},
  year      = {2008},
  pages     = {954-962},
  ee        = {http://doi.acm.org/10.1145/1401890.1402004},
  crossref  = {DBLP:conf/kdd/2008},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{kbs/KhanCRPA10,
  author    = {M. Sulaiman Khan and
               Frans Coenen and
               David Reid and
               R. Patel and
               L. Archer},
  title     = {A sliding windows based dual support framework for discovering
               emerging trends from temporal data},
  journal   = {Knowl.-Based Syst.},
  volume    = {23},
  number    = {4},
  year      = {2010},
  pages     = {316-322},
  ee        = {http://dx.doi.org/10.1016/j.knosys.2009.11.005},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{kais/HerreraCGJ11,
  author    = {Francisco Herrera and
               Crist{\'o}bal J. Carmona and
               Pedro Gonz{\'a}lez and
               Mar\'{\i}a Jos{\'e} del Jes{\'u}s},
  title     = {An overview on subgroup discovery: foundations and applications},
  journal   = {Knowl. Inf. Syst.},
  volume    = {29},
  number    = {3},
  year      = {2011},
  pages     = {495-525},
  ee        = {http://dx.doi.org/10.1007/s10115-010-0356-2},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{ipl/BaileyL10,
  author    = {James Bailey and
               Elsa Loekito},
  title     = {Efficient incremental mining of contrast patterns in changing
               data},
  journal   = {Inf. Process. Lett.},
  volume    = {110},
  number    = {3},
  year      = {2010},
  pages     = {88-92},
  ee        = {http://dx.doi.org/10.1016/j.ipl.2009.10.012},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{eswa/LiuSLL09,
  author    = {Duen-Ren Liu and
               Meng-Jung Shih and
               Churn-Jung Liau and
               Chin-Hui Lai},
  title     = {Mining the change of event trends for decision support in
               environmental scanning},
  journal   = {Expert Syst. Appl.},
  volume    = {36},
  number    = {2},
  year      = {2009},
  pages     = {972-984},
  ee        = {http://dx.doi.org/10.1016/j.eswa.2007.10.016},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{datamine/YangWZ06,
  author    = {Ying Yang and
               Xindong Wu and
               Xingquan Zhu},
  title     = {Mining in Anticipation for Concept Change: Proactive-Reactive
               Prediction in Data Streams},
  journal   = {Data Min. Knowl. Discov.},
  volume    = {13},
  number    = {3},
  year      = {2006},
  pages     = {261-289},
  ee        = {http://dx.doi.org/10.1007/s10618-006-0050-x},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{aiia/AppiceCMM07,
  author    = {Annalisa Appice and
               Michelangelo Ceci and
               Carlo Malgieri and
               Donato Malerba},
  title     = {Discovering Relational Emerging Patterns},
  booktitle = {Artificial Intelligence and Human-Oriented Computing,
               10th Congress of the Italian Association for Artificial
               Intelligence (AI*IA)},
  year      = {2007},
  pages     = {206-217},
  ee        = {http://dx.doi.org/10.1007/978-3-540-74782-6_19},
  crossref  = {DBLP:conf/aiia/2007},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@inproceedings{sebd/CeciAMM08,
  author    = {Michelangelo Ceci and
               Annalisa Appice and
               Lucrezia Macchia and
               Donato Malerba},
  title     = {Relational Classification based on Emerging Patterns},
  booktitle = {Sixteenth Italian Symposium on Advanced
               Database Systems (SEBD)},
  year      = {2008},
  pages     = {45-56},
  crossref  = {DBLP:conf/sebd/2008},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{dexa/CeciAM08,
  author    = {Michelangelo Ceci and
               Annalisa Appice and
               Donato Malerba},
  title     = {Emerging Pattern Based Classification in Relational Data
               Mining},
  booktitle = {19th International
               Conference on Database and Expert Systems Applications (DEXA)},
  year      = {2008},
  pages     = {283-296},
  ee        = {http://dx.doi.org/10.1007/978-3-540-85654-2_28},
  crossref  = {DBLP:conf/dexa/2008},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{ismis/CeciACM08,
  author    = {Michelangelo Ceci and
               Annalisa Appice and
               Costantina Caruso and
               Donato Malerba},
  title     = {Discovering Emerging Patterns for Anomaly Detection in Network
               Connection Data},
  booktitle = {17th International Symposium Foundations of Intelligent Systems (ISMIS)},
  year      = {2008},
  pages     = {179-188},
  ee        = {http://dx.doi.org/10.1007/978-3-540-68123-6_20},
  crossref  = {DBLP:conf/ismis/2008},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@article{ChuTL09temporalEP,
  author    = {Chun-Jung Chu and
               Vincent S. Tseng and
               Tyne Liang},
  title     = {Efficient mining of temporal emerging itemsets from data
               streams},
  journal   = {Expert Syst. Appl.},
  volume    = {36},
  number    = {1},
  year      = {2009},
  pages     = {885-893},
  ee        = {http://dx.doi.org/10.1016/j.eswa.2007.10.040},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@INPROCEEDINGS{KimEPClusteringMicroarray04,
  AUTHOR =       {Young Bun Kim and Jung Hun Oh and Jean Gao},
  TITLE =        {Emerging pattern based subspace clustering of microarray gene expression data using mixture models},
  BOOKTITLE =    {International Conference on Advances in Bioinformatics and its Applications},
  YEAR =         {2004}
}

@article{BoulesteixT06,
  author    = {Anne-Laure Boulesteix and
               Gerhard Tutz},
  title     = {Identification of interaction patterns and classification
               with applications to microarray data},
  journal   = {Computational Statistics {\&} Data Analysis},
  volume    = {50},
  number    = {3},
  year      = {2006},
  pages     = {783-802},
  ee        = {http://dx.doi.org/10.1016/j.csda.2004.10.004},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{pkddCeciAM07,
  author    = {Michelangelo Ceci and Annalisa Appice and
               Donato Malerba},
  title     = {Discovering Emerging Patterns in Spatial Databases: A Multi-relational Approach},
  booktitle = {11th European
               Conference on Principles and Practice of Knowledge Discovery
               in Databases (PKDD)},
  year      = {2007},
  pages     = {390-397},
  ee        = {http://dx.doi.org/10.1007/978-3-540-74976-9_38},
  crossref  = {DBLP:conf/pkdd/2007},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{cikm/AlqadahB08a,
  author    = {Faris Alqadah and
               Raj Bhatnagar},
  title     = {Detecting significant distinguishing sets among bi-clusters},
  booktitle = {17th ACM Conference on Information and
               Knowledge Management (CIKM)},
  year      = {2008},
  pages     = {1455-1456},
  ee        = {http://doi.acm.org/10.1145/1458082.1458330},
  crossref  = {DBLP:conf/cikm/2008},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{AlqadahB09,
  author    = {Faris Alqadah and
               Raj Bhatnagar},
  title     = {Discovering Substantial Distinctions among Incremental Bi-Clusters},
  booktitle = {SIAM International Conference on Data
               Mining (SDM)},
  year      = {2009},
  pages     = {197-208},
  ee        = {http://www.siam.org/proceedings/datamining/2009/dm09_021_alqadahf.pdf},
  crossref  = {DBLP:conf/sdm/2009},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@TECHREPORT{FangKumarDenseMine09,
  AUTHOR =       {Gang Fang
   and Gaurav Pandey
   and Wen Wang and
   Manish Gupta and
   Michael Steinbach and Vipin Kumar},
  TITLE =        {Mining Low-Support Discriminative Patterns from Dense and High-Dimensional Data},
  INSTITUTION =  {University of Minnesota - Computer Science and Engineering},
  YEAR =         {2009},
  type =         {Technical Report},
  number =       {09-011},
}


@article{Gu2010533EPactivity,
title = "An unsupervised approach to activity recognition and segmentation based on object-use fingerprints",
journal = "Data \& Knowledge Engineering",
volume = "69",
number = "6",
pages = "533 - 544",
year = "2010",
note = "",
issn = "0169-023X",
doi = "10.1016/j.datak.2010.01.004",
url = "http://www.sciencedirect.com/science/article/pii/S0169023X10000133",
author = "Tao Gu and Shaxun Chen and Xianping Tao and Jian Lu",
keywords = "Human activity recognition",
keywords = "Activity trace segmentation",
keywords = "Contrast patterns",
keywords = "Emerging patterns",
keywords = "Fingerprint",
keywords = "Object-use",
keywords = "Web mining",
keywords = "RFID"
}

@article{AuerECPMedChem08,
author = {Jens Auer and Jurgen Bajorath},
title = {Simulation of sequential screening experiments using emerging chemical patterns},
journal = {Medicinal Chemistry},
volume = {4},
number = {1},
pages = {80-90},
year = {2008},
abstract={A method called "Emerging Chemical Patterns" (ECP) has recently been introduced as a novel approach
to binary molecular classification (for example, "active" versus "inactive"). The underlying pattern recognition
algorithm was first introduced in computer science and then adopted for applications in medicinal chemistry
and compound screening. A special feature is its ability to accurately classify molecules on the basis of very
small training sets containing only a few compounds. This feature is highly relevant for virtual compound
screening when only very few experimental hits are available as templates. Here we adopt ECP calculations
to simulate sequential screening using an experimental high-throughput screening (HTS) data set containing
inhibitors of dihydrofolate reductase. In doing so, we focus on minimizing the number of database compounds
that need to be evaluated in order to identify a substantial fraction of available hits. We demonstrate that
iterative ECP calculations recover on average between approximately 19% and approximately 39% of available
hits in the data set while dramatically reducing the number of compounds that need to be tested to between
approximately 0.002% and approximately 9% of the screening database.}
}

@article{AuerBEPCompound06,
author = {Jens Auer and Jurgen Bajorath},
title = {Emerging Chemical Patterns: A New Methodology for Molecular Classification and Compound Selection},
journal = {Journal of Chemical Information and Modeling},
volume = {46},
number = {6},
pages = {2502-2514},
year = {2006},
doi = {10.1021/ci600301t},
URL = {http://pubs.acs.org/doi/abs/10.1021/ci600301t},
eprint = {http://pubs.acs.org/doi/pdf/10.1021/ci600301t}
}

@article{AuerBEPCompound08,
author = {Jens Auer and Jurgen Bajorath},
title = {Distinguishing between Bioactive and Modeled Compound Conformations through Mining of Emerging Chemical Patterns},
journal = {Journal of Chemical Information and Modeling},
volume = {48},
number = {9},
pages = {1747-1753},
year = {2008},
doi = {10.1021/ci8001793},
URL = {http://pubs.acs.org/doi/abs/10.1021/ci8001793},
eprint = {http://pubs.acs.org/doi/pdf/10.1021/ci8001793}
}

@article{Sylvain2010QSARJumpingFrags,
author = {Sylvain Lozano and Poezevara, Guillaume and Halm-Lemeille, Marie-Pierre and Lescot-Fontaine, Elodie and Lepailleur, Alban and Bissell-Siders, Ryan and Cre´milleux, Bruno and Rault, Sylvain and Cuissart, Bertrand and Bureau, Ronan},
title = {Introduction of Jumping Fragments in Combination with QSARs for the Assessment of Classification in Ecotoxicology},
journal = {Journal of Chemical Information and Modeling},
volume = {50},
number = {8},
pages = {1330-1339},
year = {2010},
doi = {10.1021/ci100092x},
URL = {http://pubs.acs.org/doi/abs/10.1021/ci100092x},
eprint = {http://pubs.acs.org/doi/pdf/10.1021/ci100092x}
}


%above: added after Dec 1 2011

@BOOK{DongBailey12,
  editor =       {Guozhu Dong and James Bailey},
  TITLE =        {Contrast Data Mining: Concepts, Algorithms, and Applications},
  PUBLISHER =    {Chapman \& Hall/CRC},
  YEAR =         {To appear in 2012},
  series =       {Data Mining and Knowledge Discovery Series}
}

@inproceedings{parameterFreeAssociativeClassifierCerfGSB08,
  author    = {Lo\"{\i}c Cerf and
               Dominique Gay and
               Nazha Selmaoui and
               Jean-Fran\c{c}ois Boulicaut},
  title     = {A Parameter-Free Associative Classification Method},
  booktitle = {10th International
               Conference on Data Warehousing and Knowledge Discovery (DaWaK)},
  year      = {2008},
  pages     = {293-304},
  ee        = {http://dx.doi.org/10.1007/978-3-540-85836-2_28},
  crossref  = {DBLP:conf/dawak/2008},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{OutputComplexityHagen07,
author = {Matthias Hagen},
title = {Lower Bounds for Three Algorithms for the Transversal Hypergraph Generation},
booktitle = {Workshop on Graph-Theoretic Concepts in Computer Science},
year = {2007},
pages = {316--327},
doi = {10.1007/978-3-540-74839-7_30},
masid = {3345309}
}

@inproceedings{ConflictBasedConfidence08,
author = {Peerapon Vateekul and M{ei-ling} Shyu},
title = {A conflict-based confidence measure for associative classification},
booktitle = {Information Reuse and Integration},
year = {2008},
pages = {256--261},
doi = {10.1109/IRI.2008.4583039},
masid = {4722716}
}

@article{KumarCharacDiscrPatterns2011,
author = {Gang Fang and Wen Wang and Benjamin Oatley and Brian Van Ness and Michael Steinbach and Vipin Kumar},
title = {Characterizing Discriminative Patterns},
journal = {Computing Research Repository},
volume = {abs/1102.4},
year = {2011},
masid = {27598295}
}

@inproceedings{DivergingContrast09,
author = {Aijun An and Qian Wan and Jiashu Zhao and Xiangji Huang},
title = {Diverging patterns: discovering significant frequency change dissimilarities in large databases},
booktitle = {International Conference on Information and Knowledge Management (CIKM)},
year = {2009},
pages = {1473--1476},
doi = {10.1145/1645953.1646148},
masid = {6030605}
}



%%% above, new 2011/09/28

@MISC{comparaPatent,
  author =       {Hardik Lagad and Guozhu Dong},
  title =        {COMPARATIVE WEB SEARCH SYSTEM AND METHOD},
  howpublished = {United States Patent 7,912,847},
  year =         {2011},
  month =        {March}
}

@PHDTHESIS{YuMSmicroarray,
  AUTHOR =       {Yu, Tsz-him},
  TITLE =        {Gene expression data and cancer correlation analysis by Emerging Pattern Based Projected Clustering},
  SCHOOL =       {Hong Kong Polytechnic University},
  YEAR =         {2005},
  type =         {{MS Thesis}}
}
%http://repository.lib.polyu.edu.hk/jspui/handle/10397/978

@PHDTHESIS{SatsangiMScontrast,
  AUTHOR =       {Satsangi, Amit},
  TITLE =        {Data Mining Using Contrast-sets: A Comparative Study},
  SCHOOL =       {University of ALBERTA},
  YEAR =         {2011},
  type =         {{MS Thesis}}
}


@PHDTHESIS{ChanMSemergingstring,
  AUTHOR =       {W{ing-yan} Sarah Chan},
  TITLE =        {Emerging substrings for sequence classification},
  SCHOOL =       {University of Hong Kong},
  YEAR =         {2003},
  type =         {{Master of Philosophy Thesis}}
}

@PHDTHESIS{TangMSmusic,
  AUTHOR =       {Fung Michael Tang},
  TITLE =        {Sequence classification and melody tracks selection},
  SCHOOL =       {University of Hong Kong},
  YEAR =         {2001},
  type =         {{Master of Philosophy Thesis}}
}



@MISC{BaileyDongTute07,
  author =       {James Bailey and Guozhu Dong},
  title =        {Contrast Data Mining: Methods and Applications},
  howpublished = {Tutorial at the IEEE International Conference on Data Mining (ICDM)},
  year =         {2007}
}

@PHDTHESIS{HamadPhD,
  AUTHOR =       {Hamad Alhammady},
  TITLE =        {The Application of Emerging Patterns in Solving Classification Problems},
  SCHOOL =       {University of Melbourne},
  YEAR =         {2005},
  type =         {{PhD Thesis}}
}

@PHDTHESIS{TerleckiPhD,
  AUTHOR =       {Pawel Terlecki},
  TITLE =        {On the Relation between Jumping Emerging Patterns and Rough Set Theory with Application to Data Classification},
  SCHOOL =       {Institute of Computer Science, Warsaw University of Technology},
  YEAR =         {2009},
  type = {{PhD Thesis}},
  note =         {Winner of Polish Prime Minister's Award for PhD thesis}
}

@PHDTHESIS{GayPhD,
  AUTHOR =       {Dominique Gay},
  TITLE =        {Constraint-based pattern mining for classification purpose},
  SCHOOL =       {Université de Nouvelle Calédonie INSA de Lyon},
  YEAR =         {2009},
  type = {{PhD Thesis}}
}



@PHDTHESIS{LoekitoPhD,
  AUTHOR =       {Elsa Loekito},
  TITLE =        {Mining Simple and Complex Patterns Efficiently
                  Using Binary Decision Diagrams},
  SCHOOL =       {University of Melbourne},
  YEAR =         {2009},
  type = {{PhD Thesis}}
}


@PHDTHESIS{LiPhD,
  AUTHOR =       {Jinyan Li},
  TITLE =        {Mining Emerging Patterns to Construct Accurate and Efficient Classifiers},
  SCHOOL =       {University of Melbourne},
  YEAR =         {2001},
  type =         {{PhD Thesis}}
}
@PHDTHESIS{JiPhD,
  AUTHOR =       {Xiaonan Ji},
  TITLE =        {Constraint Based Sequential Pattern Mining and its Applications},
  SCHOOL =       {University of Melbourne},
  YEAR =         {2008},
  type =         {{PhD Thesis}}
}



@PHDTHESIS{ZhangPhD,
  AUTHOR =       {Xiuzhen Zhang},
 TITLE =        {Emerging Patterns: Efficient Constraint-Based Mining and the Aggregation Approach for Classification},
  SCHOOL =       {University of Melbourne},
  YEAR =         {2001},
  type =         {{PhD Thesis}}
}


@PHDTHESIS{MaoPhD,
  AUTHOR =       {Shihong Mao},
  TITLE =        {Comparative Microarray Data Mining},
  SCHOOL =       {Wright State University},
  YEAR =         {2007},
  type =         {{PhD Thesis}}
}

@PHDTHESIS{KimPhD,
  AUTHOR =       {Young Bun Kim},
  TITLE =        {Comprehensive Data Analysis for Biomarker Pattern Discovery Using
  {DNA}/Protein Microarrays},
  SCHOOL =       {University of Texas at Arlington},
  YEAR =         {2008},
  type =         {{PhD Thesis}},
  abstract={During the last decade, the advent of microarray technology has stimulated
rapid research advances in bioinformatics. Microarray data pose great challenges
for computational data analysis, because of their large dimensionality (up to several
tens of thousands of genes) and their small sample sizes. In order to deal with these
particular characteristics of microarray data, the need and importance for feature
selection techniques were realized. While a lot of research deals with classi¯cation
methods and their application to microarray data, only a few approaches are ex-
plicitly designed to consider interaction among the investigated features. It is well
known that the interactions between genes or proteins are important for many bio-
logical functions, i.e. signals from the outside of a cell are mediated to the core of
the cell by protein-protein interactions of the signaling molecules. Hence, to achieve
optimal classi¯cation accuracy, these interactions among features need to be taken
into account. My research goal is to develop algorithms which not only e®ectively
select the most informative features but also identify the relationship among those
features.
For the clustering of the genes, researchers have attempted to apply feature
subset selection to select a subset of genes that are common for all possible un-known
classes. However, the fact that a certain set of genes may be only related to a subset
of experiments due to experiment design and no enough knowledge on gene function
is overlooked. In the thesis, a new subspace semi-supervised clustering algorithm
called EPSCMIX (Emerging Pattern Subspace Clustering by MIXure models) is de-
signed. This algorithm is used to ¯nd gene expression patterns which in turn could
be used to predict pathological phenotypes and identify genes that might anticipate
the clinical behavior of diseases. Our method is based on feature saliency measure,
the probability of feature relevance, which is estimated by an Expectation Maximiza-
tion (EM) algorithm. This approach employs Emerging Patterns (EPs) to identify
e®ectively relationships among genes. The best number of classes and the relevant
set of genes are discovered by EPSCMIX.
To address the problem of identifying informative genes from a large amount
of gene expression data when no prior knowledge is available, we develop a hybrid
methodology for unsupervised gene (feature) selection and sample clustering. The
algorithm, PFSBEM (hybrid PCA based Feature Selection and Boost-Expectation-
Maximization clustering), introduces a new PCA (principal component analysis)
based feature selection within a wrapper framework. PFSBEM uses a three-step
approach to feature selection and data clustering. The first step initially reduces
high-dimension feature space by retrieving feature subsets with original physical
meaning based on their capacities to reproduce sample projections on PCs (principal
components). Each feature subset corresponds to a certain PC. The second step
then determines the important PCs that contribute to data clustering. A boost-
EM (expectation-maximization) clustering method is developed to achieve stable
data grouping. Finally, from the merged feature subsets of important PCs, the best
feature subset that maximizes data clustering is selected.
Feature pattern (combination of features) identi¯cation techniques could be
used to capture more underlying semantics than single feature. However, it is very
hard to ¯nd meaningful patterns in large datasets like microarray data because of
the huge search space. Furthermore, infrequent patterns are often irrelevant or do
not improve the accuracy of the classi¯cation. To tackle these problems, we ¯nally
design a discriminative feature patterns identi¯cation system named DFPIS. Instead
of simply identifying genes contributing to the network, this methodology takes into
consideration of gene interactions which are represented as Strong Jumping Emerging
Patterns (SJEP). Furthermore, infrequent patterns though occurred are considered
irrelevant. The whole framework consists of three steps: feature (gene, protein)
selection, feature pattern identi¯cation, and pattern annotation.
}
}

@PHDTHESIS{MiltonPhD,
  AUTHOR =       {Milton Garcia-Borroto},
  TITLE =        {Searching Extended Emerging Patterns
for Supervised Classification},
  SCHOOL =       {Computer Science Department, National
Institute for Astrophysics Optics and Electronics},
  YEAR =         {2010},
  type =         {{PhD Thesis}},
  address =      {Puebla, Mexico}
}


@PHDTHESIS{NedjarPhD,
  AUTHOR =       {S{\'e}bastien Nedjar},
  TITLE =        {Cubes Emergents pour l'analyse des
renversements de tendances dans les bases de donnees
multidimensionnelles},
  SCHOOL =       {Doctorat Aix-Marseille Universite delivre par
l' Universite de la Mediterranee},
  YEAR =         {2009},
  type =         {{PhD Thesis}}
}


@PHDTHESIS{HongChengPhD09,
  AUTHOR =       {Hong Cheng},
  TITLE =        {Towards Accurate and Efficient Classification: A Discriminative
and Frequent Pattern-Based Approach},
  SCHOOL =       {University of Illinios at Urbana-Champion (UIUC)},
  YEAR =         {2009},
  type =         {{PhD Thesis}},
  note =         {Recipient of finalist for 2009 ACM SIGKDD Doctoral Dissertation Award
Competition},
}


@PHDTHESIS{FanPhD,
  AUTHOR =       {Hongjian Fan},
  TITLE =        {Efficient Mining of Interesting Emerging Patterns
and Their Effective Use in Classification},
  SCHOOL =       {University of Melbourne},
  YEAR =         {2004},
  type =         {{PhD Thesis}},
  month =        {May}
}

@inproceedings{DongLiZDRS99,
  author    = {Jinyan Li and
               Xiuzhen Zhang and
               Guozhu Dong and
               Kotagiri Ramamohanarao and
               Qun Sun},
  title     = {Efficient Mining of High Confidience Association Rules without
               Support Thresholds},
  booktitle = {European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD)},
  year      = {1999},
  pages     = {406-411}
}


@inproceedings{DongL99,
  author    = {Guozhu Dong and
               Jinyan Li},
  title     = {Efficient Mining of Emerging Patterns: Discovering Trends
               and Differences},
  booktitle = {ACM International Conference on Knowledge Discovery and Data Mining (KDD)},
  year      = {1999},
  pages     = {43-52}
}

@inproceedings{DongZWL99,
  author    = {Guozhu Dong and
               Xiuzhen Zhang and
               Limsoon Wong and
               Jinyan Li},
  title     = {{CAEP}: Classification by Aggregating Emerging Patterns},
  booktitle = {Discovery Science},
  year      = {1999},
  pages     = {30-42}
}

@inproceedings{DongLiDR00pkdd,
  author    = {Jinyan Li and
               Guozhu Dong and
               Kotagiri Ramamohanarao},
  title     = {Instance-Based Classification by Emerging Patterns},
  booktitle = {European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD)},
  year      = {2000},
  pages     = {191-200},
  ee        = {http://link.springer.de/link/service/series/0558/bibs/1910/19100191.htm},
  crossref  = {DBLP:conf/pkdd/2000},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{DongLiDR00,
  author    = {Jinyan Li and
               Guozhu Dong and
               Kotagiri Ramamohanarao},
  title     = {Making Use of the Most Expressive Jumping Emerging Patterns
               for Classification},
  booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)},
  year      = {2000},
  pages     = {220-232}
}


@inproceedings{DongZhangDR00,
  author    = {Xiuzhen Zhang and
               Guozhu Dong and
               Kotagiri Ramamohanarao},
  title     = {Exploring constraints to efficiently mine emerging patterns
               from large high-dimensional datasets},
  booktitle = {ACM International Conference on Knowledge Discovery and Data Mining (KDD)},
  year      = {2000},
  pages     = {310-314}
}


@inproceedings{DongZhangDR00ideal,
  author    = {Xiuzhen Zhang and
               Guozhu Dong and
               Kotagiri Ramamohanarao},
  title     = {Information-Based Classification by Aggregating Emerging
               Patterns},
  booktitle = {Intelligent Data Engineering and Automated Learning ({IDEAL})},
  year      = {2000},
  pages     = {48-53}
}

@inproceedings{DongLiRD00,
  author    = {Jinyan Li and
               Kotagiri Ramamohanarao and
               Guozhu Dong},
  title     = {Emerging Patterns and Classification},
  booktitle = {Asian Computing Science Conference},
  year      = {2000},
  pages     = {15-32}
}

@article{DongLiDR01,
  author    = {Jinyan Li and
               Guozhu Dong and
               Kotagiri Ramamohanarao},
  title     = {Making Use of the Most Expressive Jumping Emerging Patterns
               for Classification},
  journal   = {Knowl. Inf. Syst.},
  volume    = {3},
  number    = {2},
  year      = {2001},
  pages     = {131-145}
}


@inproceedings{DongZhangDR01,
  author    = {Xiuzhen Zhang and
               Guozhu Dong and
               Kotagiri Ramamohanarao},
  title     = {Building Behaviour Knowledge Space to Make Classification
               Decision},
  booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)},
  year      = {2001},
  pages     = {488-494}
}


@inproceedings{DongLiRD01,
  author    = {Jinyan Li and
               Kotagiri Ramamohanarao and
               Guozhu Dong},
  title     = {Combining the Strength of Pattern Frequency and Distance
               for Classification},
  booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)},
  year      = {2001},
  pages     = {455-466}
}


@inproceedings{DongDespD01,
  author    = {Guozhu Dong and
               Kaustubh Deshpande},
  title     = {Efficient Mining of Niches and Set Routines},
  booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)},
  year      = {2001},
  pages     = {234-246}
}


@article{DongLiDRW04,
  author    = {Jinyan Li and
               Guozhu Dong and
               Kotagiri Ramamohanarao and
               Limsoon Wong},
  title     = {{DeEPs}: A New Instance-Based Lazy Discovery and Classification
               System},
  journal   = {Machine Learning},
  volume    = {54},
  number    = {2},
  year      = {2004},
  pages     = {99-124}
}


@article{DongLiMDR04,
  author    = {Jinyan Li and
               Thomas Manoukian and
               Guozhu Dong and
               Kotagiri Ramamohanarao},
  title     = {Incremental Maintenance on the Border of the Space of Emerging
               Patterns},
  journal   = {Data Min. Knowl. Discov.},
  volume    = {9},
  number    = {1},
  year      = {2004},
  pages     = {89-116}
}


@article{DongWangZDL05,
  author    = {Lusheng Wang and
               Hao Zhao and
               Guozhu Dong and
               Jianping Li},
  title     = {On the complexity of finding emerging patterns},
  journal   = {Theor. Comput. Sci.},
  volume    = {335},
  number    = {1},
  year      = {2005},
  pages     = {15-27}
}


@article{DongLKAIS05,
  author    = {Guozhu Dong and
               Jinyan Li},
  title     = {Mining border descriptions of emerging patterns from dataset
               pairs},
  journal   = {Knowl. Inf. Syst.},
  volume    = {8},
  number    = {2},
  year      = {2005},
  pages     = {178-202}
}


@article{DongMaoD05,
  author    = {Shihong Mao and
               Guozhu Dong},
  title     = {Discovery of Highly Differentiative Gene Groups from Microarray
               Gene Expression Data Using the Gene Club Approach},
  journal   = {J. Bioinformatics and Computational Biology},
  volume    = {3},
  number    = {6},
  year      = {2005},
  pages     = {1263-1280}
}


@inproceedings{DongJiBD05,
  author    = {Xiaonan Ji and
               James Bailey and
               Guozhu Dong},
  title     = {Mining Minimal Distinguishing Subsequence Patterns with
               Gap Constraints},
  booktitle = {IEEE International Conference on Data Mining (ICDM)},
  year      = {2005},
  pages     = {194-201}
  }
@INPROCEEDINGS{DongChen06OneClass,
  AUTHOR =       {Lijun Chen and Guozhu Dong},
  TITLE =        {Masquerader Detection Using {OCLEP}: One Class Classification Using Length Statistics of Emerging Patterns},
  BOOKTITLE =    {International Workshop on INformation Processing over Evolving Networks (WINPEN)},
  YEAR =         {2006},
 }


@article{DongJiBD07,
  author    = {Xiaonan Ji and
               James Bailey and
               Guozhu Dong},
  title     = {Mining minimal distinguishing subsequence patterns with
               gap constraints},
  journal   = {Knowl. Inf. Syst.},
  volume    = {11},
  number    = {3},
  year      = {2007},
  pages     = {259-286}
}


@article{MaoWD09concord,
  author    = {Shihong Mao and
               Charles Wang and
               Guozhu Dong},
  title     = {Evaluation of Inter-Laboratory and Cross-Platform concordance
               of {DNA} microarrays through Discriminating genes and Classifier
               transferability},
  journal   = {J. Bioinformatics and Computational Biology},
  volume    = {7},
  number    = {1},
  year      = {2009},
  pages     = {157-173}
}


@incollection{kddworkshop/ImbermanT04,
  author    = {Susan P. Imberman and
               Abdullah Uz Tansel and Eric Pacuit},
  title     = {{NUWEP} -- An Efficient Method For Finding Emerging Large Itemsets},
  booktitle = {Third Workshop on Mining Temporal and Sequential Data at SIGKDD},
  year      = {2004}
}

@incollection{DongLencyclopedia09EPClassify,
  author    = {Guozhu Dong and
               Jinyan Li},
  title     = {Emerging Pattern Based Classification},
  booktitle = {Encyclopedia of Database Systems},
  year      = {2009},
  pages     = {985},
  ee        = {http://dx.doi.org/10.1007/978-0-387-39940-9_5002},
  crossref  = {DBLP:reference/db/2009},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@incollection{DongLencyclopedia09microarray,
  author    = {Guozhu Dong and
               Jinyan Li},
  title     = {Applications of Emerging Patterns for Microarray Gene Expression
               Data Analysis},
  booktitle = {Encyclopedia of Database Systems},
  year      = {2009},
  pages     = {107},
  ee        = {http://dx.doi.org/10.1007/978-0-387-39940-9_5003},
  crossref  = {DBLP:reference/db/2009},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@inproceedings{DongLiuICDM09,
  author    = {Qingbao Liu and
               Guozhu Dong},
  title     = {A Contrast Pattern Based Clustering Quality Index for Categorical
               Data},
  booktitle = {IEEE International Conference on Data Mining (ICDM)},
  year      = {2009},
  pages     = {860-865}
}

@INBOOK{DongCondContrast09,
  AUTHOR =       {Guozhu Dong and Jinyan Li and Guimei Liu and Limsoon Wong},
  TITLE =        {Mining Conditional Contrast Patterns. {Chapter in Post-Mining of Association Rules: Techniques for Effective Knowledge
Extraction}},
  PUBLISHER =    {Yanchang Zhao and Chengqi Zhang and Longbing Cao eds. IGI Global},
  YEAR =         {2009}
}

@inproceedings{DongSSBP10,
  author    = {Guozhu Dong and
               Ting Sa},
  title     = {Analyzing and Tracking Weblog Communities Using Discriminative
               Collection Representatives},
  booktitle = {Advances in Social Computing, Third International Conference
               on Social Computing, Behavioral Modeling, and Prediction,
               (SBP)},
  year      = {2010},
  pages     = {256-264}
}

@inproceedings{DongFore11,
  author    = {Neil Fore and
               Guozhu Dong},
  title     = {{CPC}: A Contrast Pattern Based Clustering Algorithm Requiring No Distance Function},
  booktitle = {Under Review},
  year      = {2011}
}

@techreport{DongFore11TR,
  author    = {Neil Fore and
               Guozhu Dong},
  title     = {{CPC}: A Contrast Pattern Based Clustering Algorithm Requiring No Distance Function},
  INSTITUTION =  {Department of Computer Science and Engineering, Wright State University},
  year      = {2011}
}

@inproceedings{DongFore11blog,
  author    = {Guozhu Dong and Neil Fore},
  title     = {Discovering Dynamic Logical Blog Communities Based on Their Distinct Interest Profiles},
  booktitle = {International Conference on Social Eco-Informatics ({SOTICS})},
  year      = {2011}
}


@INPROCEEDINGS{BringmannPatternBClassify,
  AUTHOR =       {Bjorn Bringmann and Siegfried Nijssen and Albrecht Zimmermann},
  TITLE =        {Pattern-Based Classification: A Unifying Perspective},
  BOOKTITLE =    {From Local Patterns to Global Models, an ECML/PKDD Workshop},
  YEAR =         {2009}
}


@inproceedings{LiW02pkdd,
  author    = {Jinyan Li and
               Limsoon Wong},
  title     = {Geography of Differences between Two Classes of Data},
  booktitle = {European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD)},
  year      = {2002},
  pages     = {325-337}
}

@article{LiW02abio,
  author    = {Jinyan Li and
               Limsoon Wong},
  title     = {Identifying good diagnostic gene groups from gene expression
               profiles using the concept of emerging patterns },
  journal   = {Bioinformatics},
  volume    = {18},
  number    = {10},
  year      = {2002},
  pages     = {1406-1407}
}

@inproceedings{LiW02icdm,
  author    = {Jinyan Li and
               Limsoon Wong},
  title     = {Solving the Fragmentation Problem of Decision Trees by Discovering
               Boundary Emerging Patterns},
  booktitle = {IEEE International Conference on Data Mining (ICDM)},
  year      = {2002},
  pages     = {653-656},
  abstract={The single coverage constraint discourages a decision tree to contain many significant rules.
  The loss of significant rules leads to a loss in accuracy. On the other hand,
  the fragmentation problem causes a decision tree to contain too many minor rules.
  The presence of minor rules decreases accuracy. We propose to use emerging patterns
  to solvethese problems. In our approach, many globally significant rules can be discovered.
  Extensive experimental results on gene expression datasets show that our approach are more accurate than
  single C4.5 trees, and are also better than bagged or boosted C4.5 trees.}
}

%12-22-11

@article{LiLDYW03bio,
  author    = {Jinyan Li and
               Huiqing Liu and
               James R. Downing and
               Allen Eng-Juh Yeoh and
               Limsoon Wong},
  title     = {Simple rules underlying gene expression profiles of more
               than six subtypes of acute lymphoblastic leukemia ({ALL})
               patients},
  journal   = {Bioinformatics},
  volume    = {19},
  number    = {1},
  year      = {2003},
  pages     = {71-78}
}


@article{LiW05ida,
  author    = {Jinyan Li and
               Limsoon Wong},
  title     = {Structural geography of the space of emerging patterns},
  journal   = {Intell. Data Anal.},
  volume    = {9},
  number    = {6},
  year      = {2005},
  pages     = {567-588}
}


@inproceedings{LiLWkdd07,
  author    = {Jinyan Li and
               Guimei Liu and
               Limsoon Wong},
  title     = {Mining statistically important equivalence classes and delta-discriminative
               emerging patterns},
  booktitle = {ACM International Conference on Knowledge Discovery and Data Mining (KDD)},
  year      = {2007},
  pages     = {430-439}
}

@inproceedings{LiLWFT05,
  author    = {Haiquan Li and
               Jinyan Li and
               Limsoon Wong and
               Mengling Feng and
               Yap-Peng Tan},
  title     = {Relative risk and odds ratio: a data mining perspective},
  booktitle = {ACM Symposium on Principles of Database Systems (PODS)},
  year      = {2005},
  pages     = {368-377}
}




@inproceedings{SunZR03,
  author    = {Qun Sun and
               Xiuzhen Zhang and
               Kotagiri Ramamohanarao},
  title     = {Noise Tolerance of {EP}-Based Classifiers},
  booktitle = {Australian Conference on Artificial Intelligence},
  year      = {2003},
  pages     = {796-806}
}



@inproceedings{BaileyMR02pkdd,
  author    = {James Bailey and
               Thomas Manoukian and
               Kotagiri Ramamohanarao},
  title     = {Fast Algorithms for Mining Emerging Patterns},
  booktitle = {European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD)},
  year      = {2002},
  pages     = {39-50}
}


@inproceedings{BaileyMR03waim,
  author    = {James Bailey and
               Thomas Manoukian and
               Kotagiri Ramamohanarao},
  title     = {Classification Using Constrained Emerging Patterns},
  booktitle = {International Conference on Web-Age Information Management (WAIM)},
  year      = {2003},
  pages     = {226-237}
}


@inproceedings{BaileyMR03,
  author    = {James Bailey and
               Thomas Manoukian and
               Kotagiri Ramamohanarao},
  title     = {A Fast Algorithm for Computing Hypergraph Transversals and
               its Application in Mining Emerging Patterns},
  booktitle = {IEEE International Conference on Data Mining (ICDM)},
  year      = {2003},
  pages     = {485-488}
}


@inproceedings{RaoLiuWWZBR10,
  author    = {Xiaoyan Liu and
               Xindong Wu and
               Huaiqing Wang and
               Rui Zhang and
               James Bailey and
               Kotagiri Ramamohanarao},
  title     = {Mining distribution change in stock order streams},
  booktitle = {IEEE International Conference on Data
               Engineering (ICDE)},
  year      = {2010},
  pages     = {105-108},
  ee        = {http://dx.doi.org/10.1109/ICDE.2010.5447901},
  crossref  = {DBLP:conf/icde/2010},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@inproceedings{BaileyRamamohanaraoB03,
  author    = {Kotagiri Ramamohanarao and
               James Bailey},
  title     = {Discovery of Emerging Patterns and Their Use in Classification},
  booktitle = {Australian Conference on Artificial Intelligence},
  year      = {2003},
  pages     = {1-12}
}

@inproceedings{RamamohanaraoALT10,
  author    = {Kotagiri Ramamohanarao},
  title     = {Contrast Pattern Mining and Its Application for Building
               Robust Classifiers},
  booktitle = {International Conference on Algorithmic Learning Theory (ALT)},
  year      = {2010},
  pages     = {33},
  ee        = {http://dx.doi.org/10.1007/978-3-642-16108-7_5},
  crossref  = {DBLP:conf/alt/2010},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{BaileyChangeBL06,
  author    = {Jeffrey Chan and
               James Bailey and
               Christopher Leckie},
  title     = {Discovering and Summarising Regions of Correlated Spatio-Temporal
               Change in Evolving Graphs},
  booktitle = {ICDM Workshops},
  year      = {2006},
  pages     = {361-365}
}

@inproceedings{BaileyTingB06,
  author    = {Roger Ming Hieng Ting and
               James Bailey},
  title     = {Mining Minimal Contrast Subgraph Patterns},
  booktitle = {SIAM International Conference on Data Mining (SDM)},
  year      = {2006}
}



@inproceedings{BaileyQianBL06,
  author    = {Xiaoyuan Qian and
               James Bailey and
               Christopher Leckie},
  title     = {Mining Generalised Emerging Patterns},
  booktitle = {Australian Conference on Artificial Intelligence},
  year      = {2006},
  pages     = {295-304}
}


@inproceedings{BaileyLoekito06zbdd,
  author    = {Elsa Loekito and
               James Bailey},
  title     = {Fast mining of high dimensional expressive contrast patterns
               using zero-suppressed binary decision diagrams},
  booktitle = {Twelfth ACM SIGKDD International Conference
               on Knowledge Discovery and Data Mining},
  year      = {2006},
  pages     = {307-316},
  ee        = {http://doi.acm.org/10.1145/1150402.1150438},
  crossref  = {DBLP:conf/kdd/2006},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{BaileyLoekitoB08,
  author    = {Elsa Loekito and
               James Bailey},
  title     = {Mining influential attributes that capture class and group
               contrast behaviour},
  booktitle = {ACM Conference on Information and
               Knowledge Management (CIKM)},
  year      = {2008},
  pages     = {971-980}
}


@inproceedings{BaileyLoekitoB09,
  author    = {Elsa Loekito and
               James Bailey},
  title     = {Using Highly Expressive Contrast Patterns for Classification
               - Is It Worthwhile?},
  booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)},
  year      = {2009},
  pages     = {483-490}
}

@article{BaileyLinc10,
  author    = {James Bailey and
               Elsa Loekito},
  title     = {Efficient incremental mining of contrast patterns in changing
               data},
  journal   = {Inf. Process. Lett.},
  volume    = {110},
  number    = {3},
  year      = {2010},
  pages     = {88-92}
}


@inproceedings{WongNgoFLW10,
  author    = {Thanh-Son Ngo and
               Mengling Feng and
               Guimei Liu and
               Limsoon Wong},
  title     = {Efficiently Finding the Best Parameter for the Emerging
               Pattern-Based Classifier PCL},
  booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)},
  year      = {2010},
  pages     = {121-133}
}

@INPROCEEDINGS{PeiSeqEarlyPrediction11,
  AUTHOR =       {Zhengzheng Xing and Jian Pei and Philip S. Yu and Ke Wang},
  TITLE =        {Extracting Interpretable Features for Early Classification on Time Series},
  BOOKTITLE =    {SIAM International Conference on Data Mining (SDM)},
  YEAR =         {2011}
}

@article{JianPeiZhenhuaLinWWW10,
 author = {Zhenhua Lin and Bin Jiang and Jian Pei and Daxin Jiang},
 title = {Mining discriminative items in multiple data streams},
 journal = {World Wide Web},
 volume = {13},
 issue = {4},
 month = {December},
 year = {2010},
 issn = {1386-145X},
 pages = {497--522},
 numpages = {26},
 url = {http://dx.doi.org/10.1007/s11280-010-0094-0},
 doi = {http://dx.doi.org/10.1007/s11280-010-0094-0},
 acmid = {1852113},
 publisher = {Kluwer Academic Publishers},
 address = {Hingham, MA, USA},
 keywords = {data mining, data streams, discriminative items},
}

@MASTERSTHESIS{ZhenhuaLinMS10,
  AUTHOR =       {Zhenhua Lin},
  TITLE =        {Mining Discriminative Items in Multiple Data Streams},
  SCHOOL =       {SFU},
  YEAR =         {2010},
  type =         {Masters Thesis}
}

@MASTERSTHESIS{KubendranathanMS09,
  AUTHOR =       {Thusjanthan Kubendranathan},
  TITLE =        {MINING MULTIDIMENSIONAL DISTINCT PATTERNS},
  SCHOOL =       {SFU},
  YEAR =         {2009},
  type =         {Masters Thesis}
}

@article{DongHLPWZ04,
  author    = {Guozhu Dong and
               Jiawei Han and
               Joyce M. W. Lam and
               Jian Pei and
               Ke Wang and
               Wei Zou},
  title     = {Mining Constrained Gradients in Large Databases},
  journal   = {IEEE Trans. Knowl. Data Eng.},
  volume    = {16},
  number    = {8},
  year      = {2004},
  pages     = {922-938}
}


@article{NedjarIS11,
  author    = {S{\'e}bastien Nedjar and
               Rosine Cicchetti and
               Lotfi Lakhal},
  title     = {Extracting semantics in {OLAP} databases using emerging cubes},
  journal   = {Information Sciences},
  volume    = {},
  number    = {},
  year      = {2011},
  pages     = {}
}


@article{NedjarCCL09,
  author    = {S{\'e}bastien Nedjar and
               Alain Casali and
               Rosine Cicchetti and
               Lotfi Lakhal},
  title     = {Reduced representations of Emerging Cubes for {OLAP} database
               mining},
  journal   = {International Journal of Business Intelligence and Data Mining},
  volume    = {4},
  number    = {3/4},
  year      = {2009},
  pages     = {267-300}
}

@article{NedjarCCL09repIS,
  author    = {S{\'e}bastien Nedjar and
               Alain Casali and
               Rosine Cicchetti and
               Lotfi Lakhal},
  title     = {Emerging Cubes: Borders, size estimations and lossless reductions},
  journal   = {Inf. Syst.},
  volume    = {34},
  number    = {6},
  year      = {2009},
  pages     = {536-550}
}

@inproceedings{NedjarCCL07,
  author    = {S{\'e}bastien Nedjar and
               Alain Casali and
               Rosine Cicchetti and
               Lotfi Lakhal},
  title     = {Emerging Cubes for Trends Analysis in {OLAP} Databases},
  booktitle = {International Conference on Data Warehousing and Knowledge Discovery (DaWaK)},
  year      = {2007},
  pages     = {135-144}
}


@article{ZhangS08DiffDetect,
  author    = {Shichao Zhang},
  title     = {Detecting Differences Between Contrast Groups},
  journal   = {IEEE Transactions on Information Technology in Biomedicine},
  volume    = {12},
  number    = {6},
  year      = {2008},
  pages     = {739-745}
}

@inproceedings{ZhangSZZWZ07measUncertain,
  author    = {Jilian Zhang and
               Shichao Zhang and
               Xiaofeng Zhu and
               Xindong Wu and
               Chengqi Zhang},
  title     = {Measuring the Uncertainty of Differences for Contrasting
               Groups},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2007},
  pages     = {1920-1921}
}

@article{ZhangSZZZZ09EstConfidence,
  author    = {Yongsong Qin and
               Shichao Zhang and
               Xiaofeng Zhu and
               Jilian Zhang and
               Chengqi Zhang},
  title     = {Estimating confidence intervals for structural differences
               between contrast groups with missing data},
  journal   = {Expert Syst. Appl.},
  volume    = {36},
  number    = {3},
  year      = {2009},
  pages     = {6431-6438}
}

@article{ZhangCJW09,
  author    = {Shichao Zhang and
               Feng Chen and
               Zhi Jin and
               Ruili Wang},
  title     = {Mining class-bridge rules based on rough sets},
  journal   = {Expert Syst. Appl.},
  volume    = {36},
  number    = {3},
  year      = {2009},
  pages     = {6453-6460}
}



@article{LiY07,
  author    = {Jinyan Li and
               Qiang Yang},
  title     = {Strong Compound-Risk Factors: Efficient Discovery Through
               Emerging Patterns and Contrast Sets},
  journal   = {IEEE Transactions on Information Technology in Biomedicine},
  volume    = {11},
  number    = {5},
  year      = {2007},
  pages     = {544-552}
}

@inproceedings{WangZFY03,
  author    = {Ke Wang and
               Senqiang Zhou and
               Ada Wai-Chee Fu and
               Jeffrey Xu Yu},
  title     = {Mining Changes of Classification by Correspondence Tracing},
  booktitle = {SIAM International Conference on Data Mining (SDM)},
  year      = {2003}
}

@inproceedings{CBA98,
  author    = {Bing Liu and
               Wynne Hsu and
               Yiming Ma},
  title     = {Integrating Classification and Association Rule Mining},
  booktitle = {ACM International Conference on Knowledge Discovery and Data Mining (KDD)},
  year      = {1998},
  pages     = {80-86},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{LiuHM01a,
  author    = {Bing Liu and
               Wynne Hsu and
               Yiming Ma},
  title     = {Discovering the set of fundamental rule changes},
  booktitle = {ACM International Conference on Knowledge Discovery and Data Mining (KDD)},
  year      = {2001},
  pages     = {335-340},
  ee        = {http://portal.acm.org/citation.cfm?id=502512.502561},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{RaghuGantiGR99,
  author    = {Venkatesh Ganti and
               Johannes Gehrke and
               Raghu Ramakrishnan},
  title     = {A Framework for Measuring Changes in Data Characteristics},
  booktitle = {ACM Symposium on Principles of Database Systems (PODS)},
  year      = {1999},
  pages     = {126-137},
  ee        = {http://doi.acm.org/10.1145/303976.303989, db/conf/pods/GantiGR99.html},
  crossref  = {DBLP:conf/pods/99},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{GantiGRL02,
  author    = {Venkatesh Ganti and
               Johannes Gehrke and
               Raghu Ramakrishnan and
               Wei-Yin Loh},
  title     = {A Framework for Measuring Differences in Data Characteristics},
  journal   = {J. Comput. Syst. Sci.},
  volume    = {64},
  number    = {3},
  year      = {2002},
  pages     = {542-578},
  ee        = {http://dx.doi.org/10.1006/jcss.2001.1808},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{LeeuwenVLS07,
  author    = {Jilles Vreeken and
               Matthijs van Leeuwen and
               Arno Siebes},
  title     = {Characterising the difference},
  booktitle = {ACM International Conference on Knowledge Discovery and Data Mining (KDD)},
  year      = {2007},
  pages     = {765-774},
  ee        = {http://doi.acm.org/10.1145/1281192.1281274},
  crossref  = {DBLP:conf/kdd/2007},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@inproceedings{LeeuwenS08,
  author    = {Matthijs van Leeuwen and
               Arno Siebes},
  title     = {StreamKrimp: Detecting Change in Data Streams},
  booktitle = {ECML/PKDD},
  year      = {2008},
  pages     = {672-687}
}

@article{Leeuwen10,
  author    = {Matthijs van Leeuwen},
  title     = {Maximal exceptions with minimal descriptions},
  journal   = {Data Min. Knowl. Discov.},
  volume    = {21},
  number    = {2},
  year      = {2010},
  pages     = {259-276}
}

%**************** process ended here 3/1/2011

@article{Terlecki10,
  author    = {Pawel Terlecki},
  title     = {On the Relation between Jumping Emerging Patterns and Rough
               Set Theory with Application to Data Classification},
  journal   = {Transactions on Rough Sets XII},
  volume    = {12},   year      = {2010},   pages     = {236-338}
}
@inproceedings{TerleckiW08adaptive,
  author    = {Pawel Terlecki and Krzysztof Walczak},
  title     = {Adaptive Classification with Jumping Emerging Patterns},
  booktitle = {Third International Conference on Rough Sets and Knowledge Technology},
  year      = {2008},   pages     = {39-46}
}
@inproceedings{TerleckiW08local,
  author    = {Pawel Terlecki and Krzysztof Walczak},
  title     = {Local Projection in Jumping Emerging Patterns Discovery in Transaction Databases},
  booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)},
  year      = {2008},   pages     = {723-730}
}
@inproceedings{TerleckiW07graphcoloring,
  author    = {Pawel Terlecki and Krzysztof Walczak},
  title     = {Jumping Emerging Pattern Induction by Means of Graph Coloring
               and Local Reducts in Transaction Databases},
  booktitle = {International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing},
  year      = {2007},   pages     = {363-370}
}
@article{TerleckiW07rough,
  author    = {Pawel Terlecki and Krzysztof Walczak},
  title     = {On the relation between rough set reducts and jumping emerging patterns},
  journal   = {Inf. Sci.},
  volume    = {177},  number    = {1},
  year      = {2007},   pages     = {74-83}
}
@article{TerleckiW07negation,
  author    = {Pawel Terlecki and Krzysztof Walczak},
  title     = {Jumping emerging patterns with negation in transaction databases
               - Classification and discovery},
  journal   = {Inf. Sci.},
  volume    = {177},  number    = {24},
  year      = {2007},  pages     = {5675-5690}
}
@inproceedings{TerleckiW06,
  author    = {Pawel Terlecki and Krzysztof Walczak},
  title     = {Local Reducts and Jumping Emerging Patterns in Relational Databases},
  booktitle = {International Conference on Rough Sets and Current Trends in Computing},
  year      = {2006},  pages     = {358-367}
}
@inproceedings{KobylinskiW10,
  author    = {Lukasz Kobylinski and Krzysztof Walczak},
  title     = {Spatial Emerging Patterns for Scene Classification},
  booktitle = {International Conference on Artificial Intelligence and Soft Computing},
  year      = {2010},  pages     = {515-522}
}
@inproceedings{KobylinskiW09,
  author    = {Lukasz Kobylinski and  Krzysztof Walczak},
  title     = {Jumping Emerging Substrings in Image Classification},
  booktitle = {International Conference on Computer Analysis of Images and Patterns},
  year      = {2009},  pages     = {732-739}
}
@inproceedings{GambinW09,
  author    = {Tomasz Gambin and Krzysztof Walczak},
  title     = {Classification based on the highest impact jumping emerging patterns},
  booktitle = {International Multiconference on Computer Science and Information Technology},
  year      = {2009},  pages     = {37-42}
}
@article{GambinW09BMC,
  author    = {Tomasz Gambin and Krzysztof Walczak},
  title     = {A new classification method using array Comparative Genome
               Hybridization data, based on the concept of Limited Jumping
               Emerging Patterns},
  journal   = {{BMC} Bioinformatics},
  volume    = {10},  number    = {S-1},
  year      = {2009}
}
@inproceedings{KobylinskiW08image,
  author    = {Lukasz Kobylinski and               Krzysztof Walczak},
  title     = {Jumping Emerging Patterns with Occurrence Count in Image
               Classification},
  booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)},
  year      = {2008},
  pages     = {904-909}
}
@inproceedings{KobylinskiW08occurcount,
  author    = {Lukasz Kobylinski and                Krzysztof Walczak},
  title     = {Efficient Mining of Jumping Emerging Patterns with Occurrence
               Counts for Classification},
  booktitle = {International                Conference on Rough Sets and Current Trends in Computing},
  year      = {2008},
  pages     = {419-428}
}

@article{WebbJMLRsurvey09,
  author    = {Petra Kralj Novak and
               Nada Lavrac and
               Geoffrey I. Webb},
  title     = {Supervised Descriptive Rule Discovery: A Unifying Survey
               of Contrast Set, Emerging Pattern and Subgroup Mining},
  journal   = {Journal of Machine Learning Research},
  volume    = {10},
  year      = {2009},
  pages     = {377-403},
  ee        = {http://doi.acm.org/10.1145/1577069.1577083},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@inproceedings{WebbImpactRuleHuangW05,
  author    = {Shiying Huang and
               Geoffrey I. Webb},
  title     = {Pruning Derivative Partial Rules During Impact Rule Discovery},
  booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)},
  year      = {2005},
  pages     = {71-80},
  ee        = {http://dx.doi.org/10.1007/11430919_10},
  crossref  = {DBLP:conf/pakdd/2005},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@inproceedings{AbudawoodF09EvalSubgDisc,
  author    = {Tarek Abudawood and
               Peter A. Flach},
  title     = {Evaluation Measures for Multi-class Subgroup Discovery},
  booktitle = {ECML/PKDD},
  year      = {2009},
  pages     = {35-50}
}


@article{BoulesteixTSCARTep03,
  author    = {Anne-Laure Boulesteix and
               Gerhard Tutz and
               Korbinian Strimmer},
  title     = {A {CART}-based approach to discover emerging patterns in microarray
               data},
  journal   = {Bioinformatics},
  volume    = {19},
  number    = {18},
  year      = {2003},
  pages     = {2465-2472}
}

@inproceedings{SimeonH07,
  author    = {Mondelle Simeon and
               Robert J. Hilderman},
  title     = {Exploratory Quantitative Contrast Set Mining: A Discretization
               Approach},
  booktitle = {IEEE International Conference on Tools with Artificial Intelligence (ICTAI)},
  year      = {2007},
  pages     = {124-131}
}

@incollection{HildermanP07,
  author    = {Robert J. Hilderman and
               Terry Peckham},
  title     = {Statistical Methodologies for Mining Potentially Interesting
               Contrast Sets},
  booktitle = {Quality Measures in Data Mining},
  year      = {2007},
  pages     = {153-177}
}

@inproceedings{FurnkranzF03RuleEval,
  author    = {Johannes F{\"u}rnkranz and
               Peter A. Flach},
  title     = {An Analysis of Rule Evaluation Metrics},
  booktitle = {International Conference on Machine Learning (ICML)},
  year      = {2003},
  pages     = {202-209}
}


@article{NovakLGK09,
  author    = {Petra Kralj Novak and
               Nada Lavrac and
               Dragan Gamberger and
               Antonija Krstacic},
  title     = {CSM-SD: Methodology for contrast set mining through subgroup
               discovery},
  journal   = {Journal of Biomedical Informatics},
  volume    = {42},
  number    = {1},
  year      = {2009},
  pages     = {113-122}
}

@inproceedings{KraljLGK07sim,
  author    = {Petra Kralj and
               Nada Lavrac and
               Dragan Gamberger and
               Antonija Krstacic},
  title     = {Contrast Set Mining for Distinguishing Between Similar Diseases},
  booktitle = {Artificial Intelligence in Medicine},
  year      = {2007},
  pages     = {109-118}
}

@inproceedings{aime/GambergerL07,
  author    = {Dragan Gamberger and
               Nada Lavrac},
  title     = {Supporting Factors in Descriptive Analysis of Brain Ischaemia},
  booktitle = {11th Conference on Artificial Intelligence in Medicine},
  year      = {2007},
  pages     = {155-159},
  ee        = {http://dx.doi.org/10.1007/978-3-540-73599-1_18},
  crossref  = {DBLP:conf/aime/2007},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{KraljLGK07brain,
  author    = {Petra Kralj and
               Nada Lavrac and
               Dragan Gamberger and
               Antonija Krstacic},
  title     = {Contrast Set Mining Through Subgroup Discovery Applied to
               Brain Ischaemina Data},
  booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)},
  year      = {2007},
  pages     = {579-586}
}

@inproceedings{SouletCR04app1,
  author    = {Arnaud Soulet and C. H{\'e}bert},
  title     = {Using Emerging Patterns from Clusters to Characterize Social Subgroups of Patients Affected by Atherosclerosis},
  booktitle = {Discovery Challenge Workshop co-located with ECML/PKDD'04},
  year      = {2004}
}

@inproceedings{SouletCR04,
  author    = {Arnaud Soulet and
               Bruno Cr{\'e}milleux and
               Fran\c{c}ois Rioult},
  title     = {Condensed Representation of {EP}s and Patterns Quantified
               by Frequency-Based Measures},
  booktitle = {International Workshop on Knowledge Discovery in Inductive Databases},
  year      = {2004},
  pages     = {173-190}
}

@inproceedings{SouletCR04pakdd,
  author    = {Arnaud Soulet and
               Bruno Cr{\'e}milleux and
               Fran\c{c}ois Rioult},
  title     = {Condensed Representation of Emerging Patterns},
  booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)},
  year      = {2004},
  pages     = {127-132}
}

@inproceedings{WebbBN03,
  author    = {Geoffrey I. Webb and
               Shane M. Butler and
               Douglas A. Newlands},
  title     = {On detecting differences between groups},
  booktitle = {ACM International Conference on Knowledge Discovery and Data Mining (KDD)},
  year      = {2003},
  pages     = {256-265}
}

@article{WongT05,
  author    = {Tzu-Tsung Wong and
               Kuo-Lung Tseng},
  title     = {Mining negative contrast sets from data with discrete attributes},
  journal   = {Expert Syst. Appl.},
  volume    = {29},
  number    = {2},
  year      = {2005},
  pages     = {401-407}
}

@inproceedings{Wrobel97,
  author    = {Stefan Wrobel},
  title     = {An Algorithm for Multi-relational Discovery of Subgroups},
  booktitle = {European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD)},
  year      = {1997},
  pages     = {78-87}
}

@inproceedings{Garcia-BorrotoTC10,
  author    = {Milton Garc\'{\i}a-Borroto and
               Jos{\'e} Francisco Mart\'{\i}nez Trinidad and
               Jes{\'u}s Ariel Carrasco-Ochoa},
  title     = {A New Emerging Pattern Mining Algorithm and Its Application
               in Supervised Classification},
  booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)},
  year      = {2010},
  pages     = {150-157},
  ee        = {http://dx.doi.org/10.1007/978-3-642-13657-3_18},
  crossref  = {DBLP:conf/pakdd/2010-1},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{Garcia-BorrotoMCMR10,
  author    = {Milton Garc\'{\i}a-Borroto and
               Jos{\'e} Francisco Mart\'{\i}nez Trinidad and
               Jes{\'u}s Ariel Carrasco-Ochoa and
               Miguel Angel Medina-P{\'e}rez and
               Jos{\'e} Ruiz-Shulcloper},
  title     = {{LCM}ine: An efficient algorithm for mining discriminative
               regularities and its application in supervised classification},
  journal   = {Pattern Recognition},
  volume    = {43},
  number    = {9},
  year      = {2010},
  pages     = {3025-3034},
  ee        = {http://dx.doi.org/10.1016/j.patcog.2010.04.008},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{RamamohanaraoDS10,
  author    = {Kotagiri Ramamohanarao},
  title     = {Contrast Pattern Mining and Its Application for Building
               Robust Classifiers},
  booktitle = {Discovery Science},
  year      = {2010},
  pages     = {380},
  ee        = {http://dx.doi.org/10.1007/978-3-642-16184-1_28},
  crossref  = {DBLP:conf/dis/2010},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{FanRTKDE06,
  author    = {Hongjian Fan and
               Kotagiri Ramamohanarao},
  title     = {Fast Discovery and the Generalization of Strong Jumping
               Emerging Patterns for Building Compact and Accurate Classifiers},
  journal   = {IEEE Trans. Knowl. Data Eng.},
  volume    = {18},
  number    = {6},
  year      = {2006},
  pages     = {721-737},
  ee        = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2006.95},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{FanR05,
  author    = {Hongjian Fan and
               Kotagiri Ramamohanarao},
  title     = {A weighting scheme based on emerging patterns for weighted
               support vector machines},
  booktitle = {IEEE International Conference on Granular Computing},
  year      = {2005},
  pages     = {435-440},
  ee        = {http://doi.ieeecomputersociety.org/10.1109/GRC.2005.1547329},
  crossref  = {DBLP:conf/grc/2005},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{FanRnoise04,
  author    = {Hongjian Fan and
               Kotagiri Ramamohanarao},
  title     = {Noise Tolerant Classification by Chi Emerging Patterns},
  booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)},
  year      = {2004},
  pages     = {201-206},
  ee        = {http://springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=3056{\&}spage=201},
  crossref  = {DBLP:conf/pakdd/2004},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@inproceedings{FanRADC03,
  author    = {Hongjian Fan and
               Kotagiri Ramamohanarao},
  title     = {A {Bayesian} Approach to Use Emerging Patterns for Classification},
  booktitle = {Australasian               Database Conference},
  year      = {2003},
  pages     = {39-48},
  ee        = {http://crpit.com/confpapers/CRPITV17Fan.pdf},
  crossref  = {DBLP:conf/adc/2003},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{FanRInterestingEPMine03,
  author    = {Hongjian Fan and
               Kotagiri Ramamohanarao},
  title     = {Efficiently Mining Interesting Emerging Patterns},
  booktitle = {International Conference on Web-Age Information Management (WAIM)},
  year      = {2003},
  pages     = {189-201},
  ee        = {http://springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=2762{\&}spage=189},
  crossref  = {DBLP:conf/waim/2003},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{FanR02,
  author    = {Hongjian Fan and
               Kotagiri Ramamohanarao},
  title     = {An Efficient Single-Scan Algorithm for Mining Essential
               Jumping Emerging Patterns for Classification},
  booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)},
  year      = {2002},
  pages     = {456-462},
  ee        = {http://link.springer.de/link/service/series/0558/bibs/2336/23360456.htm},
  crossref  = {DBLP:conf/pakdd/2002},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@inproceedings{FanWangFR04,
  author    = {Zhou Wang and
               Hongjian Fan and
               Kotagiri Ramamohanarao},
  title     = {Exploiting Maximal Emerging Patterns for Classification},
  booktitle = {Australian Conference on Artificial Intelligence},
  year      = {2004},
  pages     = {1062-1068},
  ee        = {http://springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=3339{\&}spage=1062},
  crossref  = {DBLP:conf/ausai/2004},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@inproceedings{RamamohanaraoF07,
  author    = {Kotagiri Ramamohanarao and
               Hongjian Fan},
  title     = {Patterns Based Classifiers},
  booktitle = {World Wide Web},
  year      = {2007},
  pages     = {71-83},
  ee        = {http://dx.doi.org/10.1007/s11280-006-0012-7},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{FanFRL06,
  author    = {Hongjian Fan and
               Ming Fan and
               Kotagiri Ramamohanarao and
               Mengxu Liu},
  title     = {Further Improving Emerging Pattern Based Classifiers Via
               Bagging},
  booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)},
  year      = {2006},
  pages     = {91-96},
  ee        = {http://dx.doi.org/10.1007/11731139_13},
  crossref  = {DBLP:conf/pakdd/2006},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}



@inproceedings{Alhammady07,
  author    = {Hamad Alhammady},
  title     = {Mining Streaming Emerging Patterns from Streaming Data},
  booktitle = {IEEE/ACS International Conference on Computer Systems
               and Applications (AICCSA)},
  year      = {2007},
  pages     = {432-436}
}

@article{AlhammadyR06,
  author    = {Hamad Alhammady and
               Kotagiri Ramamohanarao},
  title     = {Using Emerging Patterns to Construct Weighted Decision Trees},
  journal   = {IEEE Trans. Knowl. Data Eng.},
  volume    = {18},
  number    = {7},
  year      = {2006},
  pages     = {865-876}
}

@inproceedings{AlhammadyR05,
  author    = {Hamad Alhammady and
               Kotagiri Ramamohanarao},
  title     = {Expanding the Training Data Space Using Emerging Patterns
               and Genetic Methods},
  booktitle = {SIAM International Conference on Data Mining (SDM)},
  year      = {2005}
}

@inproceedings{webiAlhammadyR05,
  author    = {Hamad Alhammady and
               Kotagiri Ramamohanarao},
  title     = {Mining Emerging Patterns and Classification in Data Streams},
  booktitle = {IEEE / WIC / ACM International Conference on Web Intelligence},
  year      = {2005},
  pages     = {272-275}
}

@inproceedings{AlhammadyR04,
  author    = {Hamad Alhammady and
               Kotagiri Ramamohanarao},
  title     = {Using Emerging Patterns and Decision Trees in Rare-Class
               Classification},
  booktitle = {IEEE International Conference on Data Mining (ICDM)},
  year      = {2004},
  pages     = {315-318},
  ee        = {http://csdl.computer.org/comp/proceedings/icdm/2004/2142/00/21420315abs.htm},
  crossref  = {DBLP:conf/icdm/2004},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{AlhammadyR04pakdd,
  author    = {Hamad Alhammady and
               Kotagiri Ramamohanarao},
  title     = {The Application of Emerging Patterns for Improving the Quality
               of Rare-Class Classification},
  booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)},
  year      = {2004},
  pages     = {207-211}
}

@inproceedings{KTDA05,
  author    = {Roman Podraza and Krzysztof Tomaszewski},
  title     = {KTDA: Emerging Patterns Based Data Analysis System},
  booktitle = {XXI Fall Meeting of Polish Information Processing Society},
  year      = {2005},
  pages     = {213-221}
}

@inproceedings{WeiDingSD10,
  author    = {Tomasz F. Stepinski and
               Josue Salazar and
               Wei Ding},
  title     = {Discovering spatio-social motifs of electoral support using
               discriminative pattern mining},
  booktitle = {1st International Conference and Exhibition
               on Computing for Geospatial Research {\&} Application ({COM.Geo})},
  year      = {2010}
}

@inproceedings{WeiDingSS09,
  author    = {Wei Ding and
               Tomasz F. Stepinski and
               Josue Salazar},
  title     = {Discovery of Geospatial Discriminating Patterns from Remote
               Sensing Datasets},
  booktitle = {SIAM International Conference on Data Mining (SDM)},
  year      = {2009},
  pages     = {425-436}
}

@inproceedings{WeiDingDE08,
  author    = {Tomasz F. Stepinski and
               Wei Ding and
               Christoph F. Eick},
  title     = {Discovering controlling factors of geospatial variables},
  booktitle = {ACM SIGSPATIAL International Symposium on Advances
               in Geographic Information Systems (GIS)},
  year      = {2008},
  pages     = {47}
}

@incollection{HanCaiCH-AttributeInduction91,
  author    = {Yandong Cai and
               Nick Cercone and
               Jiawei Han},
  title     = {Attribute-Oriented Induction in Relational Databases},
  booktitle = {Knowledge Discovery in Databases},
  publisher = {AAAI/MIT Press},
  year      = {1991},
  isbn      = {0-262-62080-4},
  pages     = {213-228},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{HanYanYH05,
  author    = {Xifeng Yan and
               Philip S. Yu and
               Jiawei Han},
  title     = {Graph indexing based on discriminative frequent structure
               analysis},
  journal   = {ACM Trans. Database Syst.},
  volume    = {30},
  number    = {4},
  year      = {2005},
  pages     = {960-993},
  ee        = {http://doi.acm.org/10.1145/1114244.1114248},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}



@inproceedings{HanLoCHKS09,
  author    = {David Lo and
               Hong Cheng and
               Jiawei Han and
               Siau-Cheng Khoo and
               Chengnian Sun},
  title     = {Classification of software behaviors for failure detection:
               a discriminative pattern mining approach},
  booktitle = {ACM International Conference
               on Knowledge Discovery and Data Mining (KDD)},
  year      = {2009},
  pages     = {557-566},
  ee        = {http://doi.acm.org/10.1145/1557019.1557083},
  crossref  = {DBLP:conf/kdd/2009},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{HanKimKWHA10,
  author    = {Hyungsul Kim and
               Sangkyum Kim and
               Tim Weninger and
               Jiawei Han and
               Tarek F. Abdelzaher},
  title     = {{NDPMine}: Efficiently Mining Discriminative Numerical Features
               for Pattern-Based Classification},
  booktitle = {ECML/PKDD},
  year      = {2010},
  pages     = {35-50},
  ee        = {http://dx.doi.org/10.1007/978-3-642-15883-4_3},
  crossref  = {DBLP:conf/pkdd/2010-2},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@inproceedings{HanLiP01,
  author    = {Wenmin Li and
               Jiawei Han and
               Jian Pei},
  title     = {{CMAR}: Accurate and Efficient Classification Based on Multiple
               Class-Association Rules},
  booktitle = {IEEE International Conference on Data Mining (ICDM)},
  year      = {2001},
  pages     = {369-376},
  ee        = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2001.989541},
  crossref  = {DBLP:conf/icdm/2001},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{HanYinH03,
  author    = {Xiaoxin Yin and
               Jiawei Han},
  title     = {{CPAR}: Classification based on Predictive Association Rules},
  booktitle = {SIAM International Conference on Data Mining (SDM)},
  year      = {2003},
  ee        = {http://www.siam.org/meetings/sdm03/proceedings/sdm03_40.pdf},
  crossref  = {DBLP:conf/sdm/2003},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{HanChengYHY08,
  author    = {Hong Cheng and
               Xifeng Yan and
               Jiawei Han and
               Philip S. Yu},
  title     = {Direct Discriminative Pattern Mining for Effective Classification},
  booktitle = {IEEE International Conference on Data Engineering},
  year      = {2008},
  pages     = {169-178},
  abstract={The application of frequent patterns in classification has demonstrated its power
  in recent studies. It often adopts a two-step approach: frequent pattern (or classification rule)
   mining followed by feature selection (or rule ranking). However, this two-step process could be
   computationally expensive, especially when the problem scale is large or the minimum support
    is low. It was observed that frequent pattern mining usually produces a huge number of
    "patterns" that could not only slow down the mining process
    but also make feature selection hard to complete. In this paper,
    we propose a direct discriminative pattern mining approach,
    DDPMine, to tackle the efficiency issue arising from the two-step approach.
    DDPMine performs a branch-and-bound search for directly mining discriminative
    patterns without generating the complete pattern set. Instead of selecting best
    patterns in a batch, we introduce a "feature-centered" mining approach that generates
    discriminative patterns sequentially on a progressively shrinking
    FP-tree by incrementally eliminating training instances. The instance elimination
    effectively reduces the problem size iteratively and expedites the mining process.
    Empirical results show that DDPMine achieves orders of magnitude speedup without
    any downgrade of classification accuracy. It outperforms the state-of-the-art
    associative classification methods in terms of both accuracy and efficiency.},
  ee        = {http://dx.doi.org/10.1109/ICDE.2008.4497425},
  crossref  = {DBLP:conf/icde/2008},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{HanFanZCGYHYV08,
  author    = {Wei Fan and
               Kun Zhang and
               Hong Cheng and
               Jing Gao and
               Xifeng Yan and
               Jiawei Han and
               Philip S. Yu and
               Olivier Verscheure},
  title     = {Direct mining of discriminative and essential frequent patterns
               via model-based search tree},
  booktitle = {ACM International Conference on Knowledge Discovery and Data Mining (KDD)},
  year      = {2008},
  pages     = {230-238},
  ee        = {http://doi.acm.org/10.1145/1401890.1401922},
  crossref  = {DBLP:conf/kdd/2008},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@inproceedings{HanChengYHH07,
  author    = {Hong Cheng and
               Xifeng Yan and
               Jiawei Han and
               Chih-Wei Hsu},
  title     = {Discriminative Frequent Pattern Analysis for Effective Classification},
  booktitle = {IEEE International Conference on Data Engineering},
  year      = {2007},
  pages     = {716-725},
  ee        = {http://dx.doi.org/10.1109/ICDE.2007.367917},
  crossref  = {DBLP:conf/icde/2007},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@inproceedings{BayPkdd99,
  author    = {Stephen D. Bay and
               Michael J. Pazzani},
  title     = {Detecting Change in Categorical Data: Mining Contrast Sets},
  booktitle = {ACM International Conference on Knowledge Discovery and Data Mining (KDD)},
  year      = {1999},
  pages     = {302-306},
  ee        = {http://doi.acm.org/10.1145/312129.312263},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@inproceedings{BayP00Erros,
  author    = {Stephen D. Bay and
               Michael J. Pazzani},
  title     = {Characterizing Model Errors and Differences},
  booktitle = {International Conference on Machine Learning (ICML)},
  year      = {2000},
  pages     = {49-56},
  crossref  = {DBLP:conf/icml/2000},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@article{BayP01,
  author    = {Stephen D. Bay and
               Michael J. Pazzani},
  title     = {Detecting Group Differences: Mining Contrast Sets},
  journal   = {Data Min. Knowl. Discov.},
  volume    = {5},
  number    = {3},
  year      = {2001},
  pages     = {213-246},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@inproceedings{BaySLangley02,
  author    = {Stephen D. Bay and
               Daniel G. Shapiro and
               Pat Langley},
  title     = {Revising Engineering Models: Combining Computational Discovery
               with Knowledge},
  booktitle = {European Conference on Machine Learning (ECML)},
  year      = {2002},
  pages     = {10-22},
  ee        = {http://link.springer.de/link/service/series/0558/bibs/2430/24300010.htm},
  crossref  = {DBLP:conf/ecml/2002},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}



@article{Webb08sig-patterns,
  author    = {Geoffrey I. Webb},
  title     = {Discovering significant patterns},
  journal   = {Machine Learning},
  volume    = {71},
  number    = {1},
  year      = {2008},
  pages     = {131},
  ee        = {http://dx.doi.org/10.1007/s10994-008-5045-y},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{Webb07sig-patterns,
  author    = {Geoffrey I. Webb},
  title     = {Discovering Significant Patterns},
  journal   = {Machine Learning},
  volume    = {68},
  number    = {1},
  year      = {2007},
  pages     = {1-33},
  ee        = {http://dx.doi.org/10.1007/s10994-007-5006-x},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{DuanZZPGcontrastInequ10,
  author    = {Lei Duan and
               Jie Zuo and
               Tianqing Zhang and
               Jing Peng and
               Jie Gong},
  title     = {Mining Contrast Inequalities in Numeric Dataset},
  booktitle = {International Conference on Web-Age Information Management (WAIM)},
  year      = {2010},
  pages     = {194-205},
  ee        = {http://dx.doi.org/10.1007/978-3-642-14246-8_21},
  crossref  = {DBLP:conf/waim/2010},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{DuanTTZZ09contrastFunction,
  author    = {Lei Duan and
               Changjie Tang and
               Liang Tang and
               Tianqing Zhang and
               Jie Zuo},
  title     = {Mining Class Contrast Functions by Gene Expression Programming},
  booktitle = {International Conference on Advanced Data Mining and Applications (ADMA)},
  year      = {2009},
  pages     = {116-127},
  ee        = {http://dx.doi.org/10.1007/978-3-642-03348-3_14},
  crossref  = {DBLP:conf/adma/2009},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{ParthasarathyO00,
  author    = {Srinivasan Parthasarathy and
               Mitsunori Ogihara},
  title     = {Exploiting Dataset Similarity for Distributed Mining},
  booktitle = {IPDPS Workshops},
  year      = {2000},
  pages     = {399-406},
  ee        = {http://link.springer.de/link/service/series/0558/bibs/1800/18000399.htm},
  crossref  = {DBLP:conf/ipps/2000w},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{LiOZ03,
  author    = {Tao Li and
               Mitsunori Ogihara and
               Shenghuo Zhu},
  title     = {Association-based similarity testing and its applications},
  journal   = {Intell. Data Anal.},
  volume    = {7},
  number    = {3},
  year      = {2003},
  pages     = {209-232},
  ee        = {http://iospress.metapress.com/openurl.asp?genre=article{\&}issn=1088-467X{\&}volume=7{\&}issue=3{\&}spage=209},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{HekkiDasM00,
  author    = {Gautam Das and
               Heikki Mannila},
  title     = {Context-Based Similarity Measures for Categorical Databases},
  booktitle = {European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD)},
  year      = {2000},
  pages     = {201-210},
  ee        = {http://link.springer.de/link/service/series/0558/bibs/1910/19100201.htm},
  crossref  = {DBLP:conf/pkdd/2000},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{HekkiDasMR98,
  author    = {Gautam Das and
               Heikki Mannila and
               Pirjo Ronkainen},
  title     = {Similarity of Attributes by External Probes},
  booktitle = {ACM International Conference on Knowledge Discovery and Data Mining (KDD)},
  year      = {1998},
  pages     = {23-29},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{KaoEstring,
  author    = {Sarah Chan and
               Ben Kao and
               Chi Lap Yip and
               Michael Tang},
  title     = {Mining Emerging Substrings},
  booktitle = {International Symposium on Database Systems for Advanced Applications (DASFAA)},
  year      = {2003},
  ee        = {http://computer.org/proceedings/dasfaa/1895/18950119abs.htm},
  crossref  = {DBLP:conf/dasfaa/2003},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@INPROCEEDINGS{Tansel04,
  author    = {Susan P. Imberman and
               Abdullah Uz Tansel and Eric Pacuit},
  TITLE =        {An Efficient Method For Finding Emerging Frequent Itemsets},
  BOOKTITLE =    {International Workshop on Mining Temporal and Sequential Data},
  YEAR =         {2004},
   pages =        {112--121}
}

@article{ImielinskiKAcubeGrades02,
  author    = {Tomasz Imielinski and
               Leonid Khachiyan and
               Amin Abdulghani},
  title     = {Cubegrades: Generalizing Association Rules},
  journal   = {Data Min. Knowl. Discov.},
  volume    = {6},
  number    = {3},
  year      = {2002},
  pages     = {219-257},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
@INPROCEEDINGS{Inakoshi02,
  AUTHOR =       {H. Inakoshi and T. Ando and A. Sato and S. Okamoto},
  TITLE =        {Discovery of emerging patterns from nearest neighbors},
  BOOKTITLE =    {International Conference on Machine Learning and Cybernetics},
  YEAR =         {2002}
}

@inproceedings{KiferBG04detectChange,
  author    = {Daniel Kifer and
               Shai Ben-David and
               Johannes Gehrke},
  title     = {Detecting Change in Data Streams},
  booktitle = {International Conference on Very Large Data Bases (VLDB)},
  year      = {2004},
  pages     = {180-191},
  ee        = {http://www.vldb.org/conf/2004/RS5P1.PDF},
  crossref  = {DBLP:conf/vldb/2004},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@ARTICLE{FriedmanBump99,
  AUTHOR =       {Jerome H. Friedman and Nicholas I. Fisher},
  TITLE =        {Bump hunting in high-dimensional data},
  JOURNAL =      {Statistics and Computing},
  YEAR =         {1999},
  volume =       {9},
  number =       {2},
  pages =        {123–143}
}

@inproceedings{LiuWMQhole98,
  author    = {Bing Liu and
               Ke Wang and
               Lai-Fun Mun and
               Xin-Zhi Qi},
  title     = {Using Decision Tree Induction for Discovering Holes in Data},
  booktitle = {Pacific Rim International Conference on Artificial Intelligence},
  year      = {1998},
  pages     = {182-193},
  ee        = {http://dx.doi.org/10.1007/BFb0095268},
  crossref  = {DBLP:conf/pricai/1998},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@inproceedings{LiuHHX00changeapp,
  author    = {Bing Liu and
               Wynne Hsu and
               Heng-Siew Han and
               Yiyuan Xia},
  title     = {Mining Changes for Real-Life Applications},
  booktitle = {International Conference on Data Warehousing and Knowledge Discovery (DaWaK)},
  year      = {2000},
  pages     = {337-346}
}


@article{ChangeCustomerBehaviorSongKK01,
  author    = {Hee Seok Song and
               Jae Kyeong Kim and
               Soung Hie Kim},
  title     = {Mining the change of customer behavior in an internet shopping
               mall},
  journal   = {Expert Syst. Appl.},
  volume    = {21},
  number    = {3},
  year      = {2001},
  pages     = {157-168},
  ee        = {http://dx.doi.org/10.1016/S0957-4174(01)00037-9},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}



@inproceedings{KeoghLinK06,
  author    = {Jessica Lin and
               Eamonn J. Keogh},
  title     = {Group {SAX}: Extending the Notion of Contrast Sets to Time
               Series and Multimedia Data},
  booktitle = {European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD)},
  year      = {2006},
  pages     = {284-296}
}

@inproceedings{ZaineDengZ10,
  author    = {Kang Deng and
               Osmar R. Za\"{\i}ane},
  title     = {An Occurrence Based Approach to Mine Emerging Sequences},
  booktitle = {International Conference on Data Warehousing and Knowledge Discovery},
  year      = {2010},
  pages     = {275-284},
  ee        = {http://dx.doi.org/10.1007/978-3-642-15105-7_22},
  crossref  = {DBLP:conf/dawak/2010},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@inproceedings{ZaineDengZ09,
  author    = {Kang Deng and
               Osmar R. Za\"{\i}ane},
  title     = {Contrasting Sequence Groups by Emerging Sequences},
  booktitle = {Discovery Science},
  year      = {2009},
  pages     = {377-384}
}

@inproceedings{ZaineSatsangiZ07,
  author    = {Amit Satsangi and
               Osmar R. Za\"{\i}ane},
  title     = {Contrasting the Contrast Sets: An Alternative Approach},
  booktitle = {International Database Engineering and Applications
               Symposium},
  year      = {2007},
  pages     = {114-119}
}

@inproceedings{adma/LeeNLSR06Ryu,
  author    = {Heon Gyu Lee and
               Kiyong Noh and
               Bum Ju Lee and
               Ho-Sun Shon and
               Keun Ho Ryu},
  title     = {Cardiovascular Disease Diagnosis Method by Emerging Patterns},
  booktitle = {Second International
               Conference on Advanced Data Mining and Applications (ADMA)},
  year      = {2006},
  pages     = {819-826},
  ee        = {http://dx.doi.org/10.1007/11811305_89},
  crossref  = {DBLP:conf/adma/2006},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{LeePRpowerloadsafety10Ryu,
  author    = {Jong Bum Lee and
               Minghao Piao and
               Keun Ho Ryu},
  title     = {Incremental Emerging Patterns Mining for Identifying Safe
               and Non-safe Power Load Lines},
  booktitle = {IEEE International Conference on Computer and Information
               Technology},
  year      = {2010},
  pages     = {1424-1429},
  ee        = {http://doi.ieeecomputersociety.org/10.1109/CIT.2010.255},
  crossref  = {DBLP:conf/IEEEcit/2010},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{YoonLK05BiodataClassification,
  author    = {Hye-Sung Yoon and
               Sang-Ho Lee and
               Ju Han Kim},
  title     = {Application of Emerging Patterns for Multi-source Bio-Data
               Classification and Analysis},
  booktitle = {International Conference on Natural Computation (ICNC)},
  year      = {2005},
  pages     = {965-974},
  ee        = {http://dx.doi.org/10.1007/11539087_128},
  crossref  = {DBLP:conf/icnc/2005-1},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{YuCCY04cancerdetection,
  author    = {Larry T. H. Yu and
               Fu-Lai Chung and
               Stephen Chi-fai Chan and
               Simon M. C. Yuen},
  title     = {Using Emerging Pattern Based Projected Clustering and Gene
               Expression Data for Cancer Detection},
  booktitle = {Asia-Pacific Bioinformatics Conference (APBC)},
  year      = {2004},
  pages     = {75-84},
  ee        = {http://crpit.com/confpapers/CRPITV29Yu.pdf},
  crossref  = {DBLP:conf/apbc/2004},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{FisherL85,
  author    = {Douglas H. Fisher and
               Pat Langley},
  title     = {Approaches to Conceptual Clustering},
  booktitle = {International Joint Conference on
               Artificial Intelligence (IJCAI)},
  year      = {1985},
  pages     = {691-697},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{MichalskiS81,
  author    = {Ryszard S. Michalski and
               Robert E. Stepp},
  title     = {An Application of {AI} Techniques to Structuring Objects into
               an Optimal Conceptual Hierarchy},
  booktitle = {International Joint Conference on
               Artificial Intelligence (IJCAI)},
  year      = {1981},
  pages     = {460-465},
  crossref  = {DBLP:conf/ijcai/1981},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{SVMVapnikCortesV95,
  author    = {Corinna Cortes and
               Vladimir Vapnik},
  title     = {Support-Vector Networks},
  journal   = {Machine Learning},
  volume    = {20},
  number    = {3},
  year      = {1995},
  pages     = {273-297},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{DecisionTreeQuinlan86,
  author    = {J. Ross Quinlan},
  title     = {Induction of Decision Trees},
  journal   = {Machine Learning},
  volume    = {1},
  number    = {1},
  year      = {1986},
  pages     = {81-106},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{KohaviJLMP94,
  author    = {Ron Kohavi and
               George H. John and
               Richard Long and
               David Manley and
               Karl Pfleger},
  title     = {{MLC++}: A Machine Learning Library in {C++}},
  booktitle = {IEEE International Conference on Tools with Artificial Intelligence (ICTAI)},
  year      = {1994},
  pages     = {740-743},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@ARTICLE{LinFuzzySVM02,
  AUTHOR =       {Chun-Fu Lin and Sheng-De Wang},
  TITLE =        {Fuzzy support vector machines},
  JOURNAL =      {IEEE Transactions on Neural Networks},
  YEAR =         {2002},
  volume =       {13},
  number =       {2},
  pages =        {464-471},
  month =        {March},
  abstract =     {A support vector machine (SVM) learns the decision surface from two distinct classes
  of the input points. In many applications, each input point may not be fully assigned to one of these two classes.
  In this paper, we apply a fuzzy membership to each input point and reformulate the SVMs such that different input
  points can make different contributions to the learning of decision surface. We call the proposed method
  fuzzy SVMs (FSVMs)}
}

@INPROCEEDINGS{LogisticContrastMining11,
  AUTHOR =       {Rajul Anand and Chandan K. Reddy},
  TITLE =        {Constrained Logistic Regression for Discriminative Pattern Mining},
  BOOKTITLE =    {European Conference on Machine Learning and Principles and
Practice of Knowledge Discovery in Databases (ECML/PKDD)},
  YEAR =         {2011}
  }
%  note =         {http://www.cs.wayne.edu/~reddy/Papers/ECMLPKDD11b.pdf}

@INPROCEEDINGS{BreastCancerRacialDisparity09,
  AUTHOR =       {Indranil Palit and Chandan K. Reddy and Kendra Schwartz},
  TITLE =        {Differential
Predictive Modeling for Racial Disparities in Breast Cancer},
  BOOKTITLE =    {IEEE International Conference on Bioinformatics and
BioMedicine (BIBM)},
  YEAR =         {2009}
  }
%  note =         {http://www.cs.wayne.edu/~reddy/Papers/BIBM09.pdf}

@article{TakizawaCrimeCAEP11,
  author    = {Atsushi Takizawa},
  title     = {Classification and feature extraction of criminal occurrence points using {CAEP} with transductive clustering},
  journal   = {Procedia - Social and Behavioral Sciences},
  volume    = {21},
  year      = {2011},
  pages     = {83-92},
  abstract={In this study, we analyze bag-snatching in Fushimi-ku and aim to improve
  the author's previous study (Takizawa et al., 2010). The quantity of natural surveillance
  from a building is calculated by considering the wall as a unit. The number of pedestrians
   on the street is estimated by the random-walk method. Some new attributes are added, and
   discriminant analysis is performed by CAEP. We optimize the area of criminal occurrence
   class separately to improve the classification accuracy of CAEP. We use a method based
   on the concept of transductive clustering. We then propose a relationship between street
   crime and micro space.}
}

@article{TakizawaKKMY07,
  author    = {Atsushi Takizawa and
               Fumie Kawaguchi and
               Naoki Katoh and
               Kenji Mori and
               Kazuo Yoshida},
  title     = {Risk discovery of car-related crimes from urban spatial
               attributes using emerging patterns},
  journal   = {KES Journal},
  volume    = {11},
  number    = {5},
  year      = {2007},
  pages     = {301-311},
  ee        = {http://iospress.metapress.com/content/3305q467pr343x31/},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@article{TakizawaKK10,
  author    = {Atsushi Takizawa and
               Wonyong Koo and
               Naoki Katoh},
  title     = {Discovering Distinctive Spatial Patterns of Snatch Theft in {Kyoto City} with {CAEP}},
  journal   = {Journal of Asian Architecture and Building Engineering},
  volume    = {9},
  number    = {1},
  year      = {2010},
  pages     = {103-110},
  ee        = {http://www.jstage.jst.go.jp/article/jaabe/9/1/9_103/_article}
}

@article{TakahashiRentCAEP09,
  author    = {Nobuyuki Takahashi and  Atsushi Takizawa and Naoki Katoh and Wonyong Koo},
  title     = {Analysis of Kansei Evaluation on Entrance Halls of Rental Office
  Buildings by {CAEP}},
  journal   = {Journal of Architecture and Planning},
  volume    = {74},
  number=640,
  year      = {2009},
  pages     = {1403-1410}
}

@article {garcia2010fep,
    author = {Garc\'{\i}a-Borroto, Milton and Mart\'{\i}nez-Trinidad, Jos\'{e} Fco. and Carrasco-Ochoa, Jes\'{u}s Ariel},
   title = {Fuzzy emerging patterns for classifying hard domains},
   journal = {Knowledge and Information Systems},
   publisher = {Springer London},
    volume = {28},
    number = {2},
   pages = {473-489},
   url = {http://dx.doi.org/10.1007/s10115-010-0324-x},
   year = {2011}
}

@inproceedings{mcpr2/Garcia-BorrotoTC10,
  author    = {Milton Garc\'{\i}a-Borroto and
               Jos{\'e} Francisco Mart\'{\i}nez Trinidad and
               Jes{\'u}s Ariel Carrasco-Ochoa},
  title     = {Cascading an Emerging Pattern Based Classifier},
  booktitle = {Advances in Pattern Recognition - Second Mexican Conference
               on Pattern Recognition (MCPR)},
  year      = {2010},
  pages     = {240-249},
  ee        = {http://dx.doi.org/10.1007/978-3-642-15992-3_26},
  crossref  = {DBLP:conf/mcpr2/2010},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{quinlan1986idt,
 author = {Quinlan, James Ross},
 year={1986},
 title = {Induction of Decision Trees},
 journal = {Mach. Learn.},
 volume = {1},
 number = {1},
 pages = {81--106},
 publisher = {Kluwer Academic Publishers},
 address = {Hingham, MA, USA},
 }

%%%%from CMAAothertopics.bib


@inproceedings{icdm/MalikK08,
  author    = {Hassan H. Malik and
               John R. Kender},
  title     = {Classifying High-Dimensional Text and Web Data Using Very
               Short Patterns},
  booktitle = {IEEE International Conference on
               Data Mining (ICDM)},
  year      = {2008},
  pages     = {923-928},
  ee        = {http://dx.doi.org/10.1109/ICDM.2008.139},
  crossref  = {DBLP:conf/icdm/2008},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  abstract={%GD -- looks like a CAEP-stye classifier using very short EPs.
  In this paper, we propose the "democratic classifier", a simple pattern-based classification algorithm that uses very short patterns for classification, and does not rely on the minimum support threshold. Borrowing ideas from democracy, our training phase allows each training instance to vote for an equal number of candidate size-2 patterns. The training instances select patterns by effectively balancing between local, class, and global significance of patterns. The selected patterns are simultaneously added to the model for all applicable classes and a novel power law based weighing scheme adjusts their weights with respect of each class. Results of experiments performed on 121 common text and Web datasets show that our algorithm almost always outperforms state of the art classification algorithms, without any parameter tuning. On 100 real-life Web datasets, the average absolute classification accuracy improvement was as great as 9.4% over SVM, Harmony, C4.5 and KNN. Also, our algorithm ran about 3.5 times faster than the fastest existing pattern-based classification algorithm.}
}

@inproceedings{ausdm/HaoQS06,
  author    = {Yalei Hao and
               Gerald Quirchmayr and
               Markus Stumptner},
  title     = {Mining {MOUCLAS} Patterns and Jumping {MOUCLAS} Patterns to
               Construct Classifiers},
  booktitle = {Data Mining - Theory, Methodology, Techniques, and Applications (Selected Papers from AusDM)},
  year      = {2006},
  pages     = {118-129},
  ee        = {http://dx.doi.org/10.1007/11677437_10},
  crossref  = {DBLP:conf/ausdm/2006lncs},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  abstract={This paper proposes a mining novel approach which consists of two new data mining algorithms for the classification over quantitative data, based on two new pattern called MOUCLAS (MOUntain function based CLASsification) Patterns and JumpingMOUCLAS Patterns. The motivation of the study is to develop two classifiers for quantitative attributes by the concepts of the association rule and the clustering. An illustration of using petroleum well logging data for oil/gas formation identification is presented in the paper. MPsandJMPs are ideally suitable to derive the implicit relationship between measured values (well logging data) and properties to be predicted (oil/gas formation or not). As a hybrid of classification and clustering and association rules mining, our approach have several advantages which are (1) it has a solid mathematical foundation and compact mathematical description of classifiers, (2) it does not require discretization, (3) it is robust when handling noisy or incomplete data in high dimensional data space. }
}

@inproceedings{cute/ParkLP10,
  author    = {Jin Hyoung Park and
               Heon Gyu Lee and
               Jong Heung Park},
  title     = {Real-Time Diagnosis System Using Incremental Emerging Pattern Mining },
  booktitle = {5th International Conference on Ubiquitous Information Technologies and Applications (CUTE) },
  year      = {2010},
  pages     = {1-5}
}

@inproceedings{interaction/XueHWMZ09,
  author    = {Jingfeng Xue and
               Changzhen Hu and
               Kunsheng Wang and
               Rui Ma and
               Jiaxin Zou},
  title     = {Metamorphic malware detection technology based on aggregating
               emerging patterns},
  booktitle = {Int. Conf. Interaction Sciences},
  year      = {2009},
  pages     = {1293-1296},
  ee        = {http://doi.acm.org/10.1145/1655925.1656162},
  crossref  = {DBLP:conf/interaction/2009},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@INCOLLECTION{LiDongEmailFilteringeEP,
  AUTHOR =       {Yan Li and Xiguang Dong},
  TITLE =        {The E-Mail Categorization and Filtering
Technology Based On {eEP}},
  BOOKTITLE =    {International Symposium on Computer Science and Computational Technology (ISCSCT)},
  YEAR =         {2010},
   pages =        {259-262}
}


@article{tkde/WangK06,
  author    = {Jianyong Wang and
               George Karypis},
  title     = {On Mining Instance-Centric Classification Rules},
  journal   = {IEEE Trans. Knowl. Data Eng.},
  volume    = {18},
  number    = {11},
  year      = {2006},
  pages     = {1497-1511},
  ee        = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2006.179},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{mldm/ShidaraNK07,
  author    = {Yohji Shidara and
               Atsuyoshi Nakamura and
               Mineichi Kudo},
  title     = {{CCIC}: Consistent Common Itemsets Classifier},
  booktitle = {5th International Conference on Machine Learning and Data Mining in Pattern Recognition
                (MLDM)},
  year      = {2007},
  pages     = {490-498},
  ee        = {http://dx.doi.org/10.1007/978-3-540-73499-4_37},
  crossref  = {DBLP:conf/mldm/2007},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@INPROCEEDINGS{Li02constructrobust,
    author = {Jiuyong Li and Rodney Topor and Hong Shen},
    title = {Construct Robust Rule Sets for Classification},
    booktitle = {ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
    year = {2002},
    pages = {564--569},
    publisher = {ACM press},
    abstract={We study the problem of computing classification rule sets from relational databases so that accurate predictions can be made on test data with missing attribute values. Traditional classifiers perform badly when test data are not as complete as the training data because they tailor a training database too much. We introduce the concept of one rule set being more robust than another, that is, able to make more accurate predictions on test data with missing attribute values. We show that the optimal class association rule set is as robust as the complete class association rule set. We then introduce the k-optimal rule set, which provides predictions exactly the same as the optimal class association rule set on test data with up to k missing attribute values. This leads to a hierarchy of k-optimal rule sets in which decreasing size corresponds to decreasing robustness, and they all more robust than a traditional classification rule set. We introduce two methods to find k-optimal rule sets, i.e. an optimal association rule mining approach and a heuristic approximate approach. We show experimentally that a k-optimal rule set generated by the optimal association rule mining approach performs better than that by the heuristic approximate approach and both rule sets perform significantly better than a typical classification rule set (C4.5Rules) on incomplete test data.}
}

@article{tkde/SunWW06,
  author    = {Yanmin Sun and
               Yang Wang and
               Andrew K. C. Wong},
  title     = {Boosting an Associative Classifier},
  journal   = {IEEE Trans. Knowl. Data Eng.},
  volume    = {18},
  number    = {7},
  year      = {2006},
  pages     = {988-992},
  ee        = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2006.105},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{tods/BaralisC04,
  author    = {Elena Baralis and
               Silvia Chiusano},
  title     = {Essential classification rule sets},
  journal   = {ACM Trans. Database Syst.},
  volume    = {29},
  number    = {4},
  year      = {2004},
  pages     = {635-674},
  ee        = {http://doi.acm.org/10.1145/1042046.1042048},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{icdm/VelosoMZ06,
  author    = {Adriano Veloso and
               Wagner Meira Jr. and
               Mohammed J. Zaki},
  title     = {Lazy Associative Classification},
  booktitle = {IEEE International Conference on
               Data Mining  (ICDM)},
  year      = {2006},
  pages     = {645-654},
  ee        = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.96},
  crossref  = {DBLP:conf/icdm/2006},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{ker/Thabtah07,
  author    = {Fadi A. Thabtah},
  title     = {A review of associative classification mining},
  journal   = {Knowledge Eng. Review},
  volume    = {22},
  number    = {1},
  year      = {2007},
  pages     = {37-65},
  ee        = {http://dx.doi.org/10.1017/S0269888907001026},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@ARTICLE{WuDuanEPbirthDefectPrediction11,
  AUTHOR =       {Baohua Wu and Lei Duan and Zhonghua Yu and Changjie Tang and Jun Zhu},
  TITLE =        {Birth defects detection algorithm based on emerging patterns},
  JOURNAL =      {Journal of Computer Applications},
  YEAR =         {2011},
  volume =       {31},
  number =       {4},
  pages =        {885-889}
}

@ARTICLE{MingFanERareClass05,
  AUTHOR =       {Ming Fan and Yanxia Liu},
  TITLE =        {Rare Class Classification Based on Essential Emerging Patterns},
  JOURNAL =      {Journal of Computer Applications (in Chinese)},
  YEAR =         {2005},
  volume =       {B12},
  pages =        {152-154}
}

@inproceedings{adc/ZhangRB10,
  author    = {Shaoyi Zhang and
               Kotagiri Ramamohanarao and
               James C. Bezdek},
  title     = {{EP}-based robust weighting scheme for fuzzy {SVM}s},
  booktitle = {Australasian Database
               Conference (ADC)},
  year      = {2010},
  pages     = {123-132},
  ee        = {http://portal.acm.org/citation.cfm?id=1862259{\&}CFID=17470975{\&}CFTOKEN=71845406},
  crossref  = {DBLP:conf/adc/2010},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{eswa/YangMSH11a,
  author    = {Guangfei Yang and
               Shingo Mabu and
               Kaoru Shimada and
               Kotaro Hirasawa},
  title     = {An evolutionary approach to rank class association rules
               with feedback mechanism},
  journal   = {Expert Syst. Appl.},
  volume    = {38},
  number    = {12},
  year      = {2011},
  pages     = {15040-15048},
  ee        = {http://dx.doi.org/10.1016/j.eswa.2011.05.042},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{pakdd/ChengCC10,
  author    = {Michael W. K. Cheng and
               Byron Choi and
               William Kwok-Wai Cheung},
  title     = {Hiding Emerging Patterns with Local Recoding Generalization},
  booktitle = {Advances in Knowledge Discovery and Data Mining (PAKDD)},
  year      = {2010},
  pages     = {158-170},
  ee        = {http://dx.doi.org/10.1007/978-3-642-13657-3_19},
  crossref  = {DBLP:conf/pakdd/2010-1},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{ismis/CeciALCFM09,
  author    = {Michelangelo Ceci and
               Annalisa Appice and
               Corrado Loglisci and
               Costantina Caruso and
               Fabio Fumarola and
               Donato Malerba},
  title     = {Novelty Detection from Evolving Complex Data Streams with
               Time Windows},
  booktitle = {International Symposium on Foundations of Intelligent Systems
               (ISMIS)},
  year      = {2009},
  pages     = {563-572},
  ee        = {http://dx.doi.org/10.1007/978-3-642-04125-9_59},
  crossref  = {DBLP:conf/ismis/2009},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@INCOLLECTION{MoritaNHY,
  AUTHOR =       {Hiroyuki Morita and Takanobu Nakahara and Hamuro and Shoji Yamamoto},
  TITLE =        {Decision Tree-based Classifier Incorporating Contrast Pattern},
  BOOKTITLE =    {IEEE International Symposium on Consumer Electronics},
  YEAR =         {2009}
}

@inproceedings{ict/AmirT10,
  author    = {Mohd. Amir and
               Durga Toshniwal},
  title     = {Instance-Based Classification of Streaming Data Using Emerging
               Patterns},
  booktitle = {International
               Conference on Information and Communication Technologies (ICT)},
  year      = {2010},
  pages     = {228-236},
  ee        = {http://dx.doi.org/10.1007/978-3-642-15766-0_33},
  crossref  = {DBLP:conf/ict/2010},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
@article{ida/Azevedo10,
  author    = {Paulo J. Azevedo},
  title     = {Rules for contrast sets},
  journal   = {Intell. Data Anal.},
  volume    = {14},
  number    = {6},
  year      = {2010},
  pages     = {623-640},
  ee        = {http://dx.doi.org/10.3233/IDA-2010-0444},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{ictai/KameyaNIS11,
  author    = {Yoshitaka Kameya and
               Satoru Nakamura and
               Tatsuya Iwasaki and
               Taisuke Sato},
  title     = {Verbal Characterization of Probabilistic Clusters Using
               Minimal Discriminative Propositions},
  booktitle = {International Conference on Tools with Artificial
               Intelligence (ICTAI)},
  year      = {2011},
  pages     = {873-875},
  ee        = {http://doi.ieeecomputersociety.org/10.1109/ICTAI.2011.136},
  crossref  = {DBLP:conf/ictai/2011},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{ClassficationComparison11,
    author = {M. Thangaraj and C. R. Vijayalakshmi},
    title = {A Study on Classification Approaches across Multiple Database Relations},
    journal = {International Journal of Computer Applications},
    year = {2011},
    volume = {12},
    number = {12},
    pages = {1--6}
}

@article{zadeh1965fzs,
   author = {Zadeh, Lofti},
   title = {Fuzzy sets},
   journal = {Information Control},
   volume = {8},
   pages = {338-353},
   year = {1965}
}

@article{dong2001lab,
   author = {Dong, Ming and Kothari, Ravi},
   title = {Look-Ahead Based Fuzzy Decision Tree Induction},
   journal = {IEEE Transactions on Fuzzy Systems},
   volume = {9},
   number = {3},
   pages = {461-468},
   year = {2001}
}



@inproceedings{cec/KameyaP11,
  author    = {Yoshitaka Kameya and
               Chativit Prayoonsri},
  title     = {Pattern-based preservation of building blocks in genetic
               algorithms},
  booktitle = {IEEE Congress on Evolutionary Computation},
  year      = {2011},
  pages     = {2578-2585},
  ee        = {http://dx.doi.org/10.1109/CEC.2011.5949939},
  crossref  = {DBLP:conf/cec/2011},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{ismis/Andruszkiewicz11,
  author    = {Piotr Andruszkiewicz},
  title     = {Lazy Approach to Privacy Preserving Classification with
               Emerging Patterns},
  booktitle = {19th International
               Symposium (ISMIS)
               Emerging Intelligent Technologies in Industry},
  year      = {2011},
  pages     = {253-268},
  ee        = {http://dx.doi.org/10.1007/978-3-642-22732-5_21},
  crossref  = {DBLP:conf/ismis/2011is},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{icip/WangWZ10,
  author    = {Liang Wang and
               Yizhou Wang and
               Debin Zhao},
  title     = {Building Emerging Pattern ({EP}) Random forest for recognition},
  booktitle = {International Conference on Image Processing
               (ICIP)},
  year      = {2010},
  pages     = {1457-1460},
  ee        = {http://dx.doi.org/10.1109/ICIP.2010.5653902},
  crossref  = {DBLP:conf/icip/2010},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{edbt/LoCL11,
  author    = {David Lo and
               Hong Cheng and
               Lucia},
  title     = {Mining closed discriminative dyadic sequential patterns},
  booktitle = {International Conference on Extending Database
               Technology (EDBT)},
  year      = {2011},
  pages     = {21-32},
  ee        = {http://doi.acm.org/10.1145/1951365.1951371},
  crossref  = {DBLP:conf/edbt/2011},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{isnn/WangLLN07,
  author    = {Haijun Wang and
               Yaping Lin and
               Xinguo Lu and
               Yalin Nie},
  title     = {A Novel {EPA-KNN} Gene Classification Algorithm},
  booktitle = {4th International
               Symposium on Neural Networks (ISNN)},
  year      = {2007},
  pages     = {1254-1263},
  ee        = {http://dx.doi.org/10.1007/978-3-540-72393-6_148},
  crossref  = {DBLP:conf/isnn/2007-2},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  abstact={Accurate classification of samples using gene expression
  frofiles is very important in cancer detection and treatment. In this paper,
  a novel EPA-KNN (Emerging Patterns Advanced-K Nearest Neighbors) gene classification algorithm is proposed. Bayes estimation is applied for the computation of entropy to improve its reliability, and RCP (Random Cut Point) is presented to strengthen the generalization about unknown test samples. With these improvements, the EPAs are acquired. Then an EPA based classifier is constructed inspired by KNN. The experimental results show the new algorithm is feasible and effective. }
}

@ARTICLE{XueCaoCAEPintrusiondetect2005,
  AUTHOR =       {Jingfeng Xue and Yuanda Cao},
  TITLE =        {Application and Research of Aggregation Emerging Pattern in Intrusion Detection},
  JOURNAL =      {Computer Application and Software},
  YEAR =         {2005},
  volume =       {22}
}

@ARTICLE{LawRongTourism2011,
  AUTHOR =       {Rob Law and Jia Rong and Huy Quan Vu and Gang Li and Hee Andy Lee},
  TITLE =        {Identifying changes and trends in {Hong Kong} outbound tourism},
  JOURNAL =      {Tourism Management},
  YEAR =         {2011},
  volume =       {32},
  number =       {5},
  pages =        {1106-1114}
}

@article{jcrd/LiuLZ07,
  author    = {Yong Liu and
               Jianzhong Li and
               Jinghua Zhu},
  title     = {A Novel Graph Classification Approach Based on Frequent Closed Emerging Patterns},
  journal   = {Journal of Computer Research and Development},
  volume    = {44},
  number    = {7},
  year      = {2007},
  pages     = {1169-1176}
  }

@inproceedings{icml/AsgharbeygiSL06,
  author    = {Nima Asgharbeygi and
               David J. Stracuzzi and
               Pat Langley},
  title     = {Relational temporal difference learning},
  booktitle = {Twenty-Third International
               Conference on Machine Learning (ICML)},
  year      = {2006},
  pages     = {49-56},
  ee        = {http://doi.acm.org/10.1145/1143844.1143851},
  crossref  = {DBLP:conf/icml/2006},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{tkde/WanA09transitional,
  author    = {Qian Wan and
               Aijun An},
  title     = {Discovering Transitional Patterns and Their Significant
               Milestones in Transaction Databases},
  journal   = {IEEE Trans. Knowl. Data Eng.},
  volume    = {21},
  number    = {12},
  year      = {2009},
  pages     = {1692-1707},
  ee        = {http://dx.doi.org/10.1109/TKDE.2009.59},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{rsfdgrc/PodrazaT07,
  author    = {Roman Podraza and
               Krzysztof Tomaszewski},
  title     = {Ordinal Credibility Coefficient - A New Approach in the
               Data Credibility Analysis},
  booktitle = {11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing},
  year      = {2007},
  pages     = {190-198},
  ee        = {http://dx.doi.org/10.1007/978-3-540-72530-5_22},
  crossref  = {DBLP:conf/rsfdgrc/2007},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@inproceedings{dawak/HebertC06,
  author    = {C{\'e}line H{\'e}bert and
               Bruno Cr{\'e}milleux},
  title     = {Optimized Rule Mining Through a Unified Framework for Interestingness
               Measures},
  booktitle = {Data Warehousing and Knowledge Discovery},
  year      = {2006},
  pages     = {238-247},
  ee        = {http://dx.doi.org/10.1007/11823728_23},
  crossref  = {DBLP:conf/dawak/2006},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}




@inproceedings{waim/ChenD06,
  author    = {Lijun Chen and
               Guozhu Dong},
  title     = {Succinct and Informative Cluster Descriptions for Document
               Repositories},
  booktitle = {7th International
               Conference on Web-Age Information Management (WAIM)},
  year      = {2006},
  pages     = {109-121},
  ee        = {http://dx.doi.org/10.1007/11775300_10},
  crossref  = {DBLP:conf/waim/2006},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@inproceedings{ictaiCelikSRS06,
  author    = {Mete Celik and
               Shashi Shekhar and
               James P. Rogers and
               James A. Shine},
  title     = {Sustained Emerging Spatio-Temporal Co-occurrence Pattern
               Mining: A Summary of Results},
  booktitle = {IEEE International Conference on Tools with Artificial
               Intelligence (ICTAI)},
  year      = {2006},
  pages     = {106-115},
  ee        = {http://doi.ieeecomputersociety.org/10.1109/ICTAI.2006.108},
  crossref  = {DBLP:conf/ictai/2006},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{TsaiS09,
  author    = {Chieh-Yuan Tsai and
               Yu-Chen Shieh},
  title     = {A change detection method for sequential patterns},
  journal   = {Decision Support Systems},
  volume    = {46},
  number    = {2},
  year      = {2009},
  pages     = {501-511},
  ee        = {http://dx.doi.org/10.1016/j.dss.2008.09.003},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{SimeonH11,
  author    = {Mondelle Simeon and
               Robert J. Hilderman},
  title     = {COSINE: A Vertical Group Difference Approach to Contrast
               Set Mining},
  booktitle = {Canadian Conference on AI},
  year      = {2011},
  pages     = {359-371},
  ee        = {http://dx.doi.org/10.1007/978-3-642-21043-3_43},
  crossref  = {DBLP:conf/ai/2011},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{mldmSimeonH11,
  author    = {Mondelle Simeon and
               Robert J. Hilderman},
  title     = {GENCCS: A Correlated Group Difference Approach to Contrast
               Set Mining},
  booktitle = {Machine Learning and Data Mining in Pattern Recognition},
  year      = {2011},
  pages     = {140-154},
  ee        = {http://dx.doi.org/10.1007/978-3-642-23199-5_11},
  crossref  = {DBLP:conf/mldm/2011},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{SimeonH07ictai,
  author    = {Mondelle Simeon and
               Robert J. Hilderman},
  title     = {Exploratory Quantitative Contrast Set Mining: A Discretization
               Approach},
  booktitle = {IEEE International Conference on Tools with Artificial
               Intelligence (ICTAI)},
  year      = {2007},
  pages     = {124-131},
  ee        = {http://doi.ieeecomputersociety.org/10.1109/ICTAI.2007.99},
  crossref  = {DBLP:conf/ictai/2007-2},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{LiZWJ06,
  author    = {Yingjiu Li and
               Sencun Zhu and
               Xiaoyang Sean Wang and
               Sushil Jajodia},
  title     = {Looking into the seeds of time: Discovering temporal patterns
               in large transaction sets},
  journal   = {Inf. Sci.},
  volume    = {176},
  number    = {8},
  year      = {2006},
  pages     = {1003-1031},
  ee        = {http://dx.doi.org/10.1016/j.ins.2005.01.019},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{CaoZZYW07,
  author    = {Longbing Cao and
               Chengqi Zhang and
               Yanchang Zhao and
               Philip S. Yu and
               Graham Williams},
  title     = {{DDDM2007}: Domain Driven Data Mining},
  journal   = {SIGKDD Explorations},
  volume    = {9},
  number    = {2},
  year      = {2007},
  pages     = {84-86},
  ee        = {http://doi.acm.org/10.1145/1345448.1345467},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{ZhuW07,
  author    = {Xingquan Zhu and
               Xindong Wu},
  title     = {Discovering Relational Patterns across Multiple Databases},
  booktitle = {International Conference on Data
               Engineering (ICDE)},
  year      = {2007},
  pages     = {726-735},
  ee        = {http://dx.doi.org/10.1109/ICDE.2007.367918},
  crossref  = {DBLP:conf/icde/2007},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@inproceedings{JinA06,
  author    = {Ruoming Jin and
               Gagan Agrawal},
  title     = {Systematic Approach for Optimizing Complex Mining Tasks
               on Multiple Databases},
  booktitle = {International Conference on Data
               Engineering},
  year      = {2006},
  pages     = {17},
  ee        = {http://doi.ieeecomputersociety.org/10.1109/ICDE.2006.154},
  crossref  = {DBLP:conf/icde/2006},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{ChangeCustomerBehaviorKim05,
  author    = {Jae Kyeong Kim and Hee Seok Song and Tae Seong Kim and Hyea Kyeong Kim},
  title     = {Detecting the change of customer behavior based on decision tree analysis},
  journal   = {Expert Syst. Appl.},
  volume    = {22},
  number    = {4},
  year      = {2005}
}

@inproceedings{AsgharbeygiSL06,
  author    = {Nima Asgharbeygi and
               David J. Stracuzzi and
               Pat Langley},
  title     = {Relational temporal difference learning},
  booktitle = {International
               Conference on Machine Learning (ICML)},
  year      = {2006},
  pages     = {49-56},
  ee        = {http://doi.acm.org/10.1145/1143844.1143851},
  crossref  = {DBLP:conf/icml/2006},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@article{GarrigaKL08,
  author    = {Gemma C. Garriga and
               Petra Kralj and
               Nada Lavrac},
  title     = {Closed Sets for Labeled Data},
  journal   = {Journal of Machine Learning Research},
  volume    = {9},
  year      = {2008},
  pages     = {559-580},
  ee        = {http://doi.acm.org/10.1145/1390681.1390700},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@article{ChenCC05change,
  author    = {Mu-Chen Chen and
               Ai-Lun Chiu and
               Hsu-Hwa Chang},
  title     = {Mining changes in customer behavior in retail marketing},
  journal   = {Expert Syst. Appl.},
  volume    = {28},
  number    = {4},
  year      = {2005},
  pages     = {773-781},
  ee        = {http://dx.doi.org/10.1016/j.eswa.2004.12.033},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{GuWTPL09,
  author    = {Tao Gu and
               Zhanqing Wu and
               XianPing Tao and
               Hung Keng Pung and
               Jian Lu},
  title     = {epSICAR: An Emerging Patterns based Approach to Sequential,
               Interleaved and Concurrent Activity Recognition},
  booktitle = {Seventh Annual IEEE International Conference on Pervasive
               Computing and Communications (PerCom)},
  year      = {2009},
  pages     = {1-9},
  ee        = {http://dx.doi.org/10.1109/PERCOM.2009.4912776},
  crossref  = {DBLP:conf/percom/2009},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{GuWWTL09,
  author    = {Tao Gu and Zhanqing Wu and Liang Wang and Xianping Tao and Jian Lu},
  title     = {Mining Emerging Patterns for recognizing activities of multiple users in pervasive computing},
  booktitle = {Mobile and Ubiquitous Systems},
  year      = {2009}
}

@inproceedings{iccs/Bissell-SidersCC10,
  author    = {Ryan Bissell-Siders and
               Bertrand Cuissart and
               Bruno Cr{\'e}milleux},
  title     = {On the Stimulation of Patterns - Definitions, Calculation
               Method and First Usages},
  booktitle = {18th International Conference on Conceptual Structures (ICCS)},
  year      = {2010},
  pages     = {56-69},
  ee        = {http://dx.doi.org/10.1007/978-3-642-14197-3_9},
  crossref  = {DBLP:conf/iccs/2010},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  abstract={We define a class of patterns generalizing the jumping emerging patterns which have been used successfully
  for classification problems but which are often absent in complex or sparse databases and which are often very specific.
  In supervised learning, the objects in a database are classified a priori into one class called positive – a target class
  – and the remaining classes, called negative. Each pattern, or set of attributes, has support in the positive class
  and in the negative class, and the ratio of these is the emergence of that pattern; the stimulating patterns are those
  patterns a, such that for many closed patterns b, adding the attributes of a to b reduces the support in the negative
  class much more than in the positive class. We present methods for comparing and attributing stimulation of closed
  patterns. We discuss the complexity of enumerating stimulating patterns. We discuss in particular the discovery of highly stimulating patterns and the discovery of patterns which capture contrasts. We extract these two types of stimulating patterns from UCI machine learning databases. }
}

@article{BrunoCremilleuxJumpingFragments2010,
  author    = {Sylvain Lozano, Guillaume Poezevara, Marie-Pierre Halm-Lemeille,
Elodie Lescot-Fontaine, Alban Lepailleur,
Ryan Bissell-Siders, Bruno Cr{\'e}milleux, Sylvain Rault, Bertrand Cuissart and Ronan Bureau},
  title     = {Introduction of Jumping Fragments in Combination with {QSAR}s for the Assessment of Classification in Ecotoxicology},
  journal   = {Journal of Chemical Information and Modeling},
  volume    = {50},
  year      = {2010},
  pages     = {1330–1339}
}

@inproceedings{DBLP:conf/ismis/PoezevaraCC09,
  author    = {Guillaume Poezevara and
               Bertrand Cuissart and
               Bruno Cr{\'e}milleux},
  title     = {Discovering Emerging Graph Patterns from Chemicals},
  booktitle = {18th International Symposium on Foundations of Intelligent Systems (ISMIS)},
  year      = {2009},
  pages     = {45-55},
  ee        = {http://dx.doi.org/10.1007/978-3-642-04125-9_8},
  crossref  = {DBLP:conf/ismis/2009},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@article{BrunoCremilleuxJIIS11frequentEPgraphs,
  author    = {Guillaume Poezevara and
               Bertrand Cuissart and
               Bruno Cr{\'e}milleux},
  title     = {Extracting and summarizing the frequent emerging graph patterns
               from a dataset of graphs},
  journal   = {J. Intell. Inf. Syst.},
  volume    = {37},
  number    = {3},
  year      = {2011},
  pages     = {333-353},
  ee        = {http://dx.doi.org/10.1007/s10844-011-0168-1},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

%%%% above -- new after 12/12/2011


@INPROCEEDINGS{HanKP11,
    author = {Jiawei Han and Micheline Kamber and Jian Pei},
    booktitle = {Data Mining: Concepts and Techniques (3rd edition)},
    year = {2011},
    publisher = {Morgan Kaufmann}
}
@inproceedings{PasquierBTL99,
  author    = {Nicolas Pasquier and
               Yves Bastide and
               Rafik Taouil and
               Lotfi Lakhal},
  title     = {Discovering Frequent Closed Itemsets for Association Rules},
  booktitle = {International Conference on Database Theory},
  year      = {1999},
  pages     = {398-416},
  ee        = {http://dx.doi.org/10.1007/3-540-49257-7_25, http://link.springer.de/link/service/series/0558/bibs/1540/15400398.htm},
  crossref  = {DBLP:conf/icdt/99},
  bibsource = {DBLP, http://dblp.uni-trier.de}
  }

@inproceedings{AgrawalIS93,
  author    = {Rakesh Agrawal and
               Tomasz Imielinski and
               Arun N. Swami},
  title     = {Mining Association Rules between Sets of Items in Large
               Databases},
  booktitle = {ACM SIGMOD International Conference
               on Management of Data},
  year      = {1993},
  pages     = {207-216},
  ee        = {http://doi.acm.org/10.1145/170035.170072, db/conf/sigmod/AgrawalIS93.html},
  crossref  = {DBLP:conf/sigmod/93},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{FayyadI93,
  author    = {Usama M. Fayyad and
               Keki B. Irani},
  title     = {Multi-Interval Discretization of Continuous-Valued Attributes
               for Classification Learning},
  booktitle = {International Joint Conference on
               Artificial Intelligence (IJCAI)},
  year      = {1993},
  pages     = {1022-1029},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{DoughertyKS95,
  author    = {James Dougherty and
               Ron Kohavi and
               Mehran Sahami},
  title     = {Supervised and Unsupervised Discretization of Continuous
               Features},
  booktitle = {International Conference on Machine Learning (ICML)},
  year      = {1995},
  pages     = {194-202},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@ARTICLE{Shannon48,
  AUTHOR =       {Claude E. Shannon},
  TITLE =        {A Mathematical Theory of Communication},
  JOURNAL =      {Bell System Technical Journal},
  YEAR =         {1948},
  volume =       {27},
  pages =        {623-656}
}
%entropy