1. Cluster Analysis

2. Purpose
clustering analysis is a process where a set of objects is partitioned into several clusters
All members in one cluster are similar to each other and different from the members of other clusters, according to some similarity metric

3. Cluster Analysis Y (Age) X (Income) Cluster Customer (Object)
Variables

4. Dissimilarity (Distance) Measure

5. Dissimilarity (Distance) Measure

6. Dissimilarity (Distance) Measure

7. Categorization of Clustering Methods
Exclusive vs. Non-Exclusive (Overlapping)
Hierarchical Methods vs. Partitioning Methods
Hierarchical Methods
Single Link Method
Complete Link Method
Partitioning Methods
Kohonen Self-Organizing Maps (SOM)
K-Means Methods
K-Medoids Methods (PAM, CLARA, CLARANS)
Demographic Methods …

8. Hierarchical Methods
Dissimilarity
Matrix (55)