Introduction to Data Mining

Chapter 5. Clustering Methods

5.1   Clustering Concepts
5.2   Clustering Vs Classification
5.3   Clustering Techniques
  5.3.1

  Partitioning Methods

5.3.1.1   k-Means
5.3.1.2   k-Medoids
5.3.1.3

  Comparing k-Means and k-Medoids

5.3.1.4

  Expectation-Maximization

5.3.2

  Hierarchical Methods

5.3.2.1

  Agglomerative Methods

5.3.2.1.1

  Dendograms

5.3.2.2

  Divisive Methods

5.3.2.2.1

  Cobweb

5.3.3

  Density-Based Methods

5.3.3.1

  DBSCAN

5.3.4

  Grid-Based Methods

5.3.4.1

  STING

5.4

  Dealing with Large Data

5.4.1   CLARA and CLARANS
5.4.2   BIRCH
5.4.3   CURE
5.4.4   CLIQUE
5.5

  Chapter Quiz

5.6

  Chapter Review Exercises

 Go to Chapter Slides