1. Chapter_20 Cluster Analysis Naresh K. Malhotra
Marketing Research-an applied orientation, 4th ed.

2. Cluster Analysis
Cluster analysis is a class of techniques used to classify objects or cases into relatively homogeneous groups called clusters.
Objects in each cluster tend to be similar to each other and dissimilar to objects in the other clusters.
Areas of Factor Analysis:
Segmenting the market
Understanding buyer behavior
Identifying new product opportunities
Selecting test markets
Reducing data

3. Conducting Cluster Analysis
It is a five-step process-
Formulate the problem
Select a distance measure
Select a clustering procedure
Decide on the number of clusters
Interpret and profile clusters
Assess the validity of clustering

4. Cluster Analysis Process
Formulate the Problem
Selecting the variables on which the clustering is based. Inclusion of even one or two irrelevant variables may distort . The distance measure determines how similar or dissimilar the objects being clustered are.

5. Cluster Analysis Process
Select a distance or similarity measure
The objective of clustering is to group similar objects together.
The methods are:
Euclidean distance or its square.
City-block or Manhattan distance
Chebychev distance

6. Cluster Analysis Process
Select a clustering procedure
Two approaches:
Hierarchical
a. Agglomerative
i. Linkage methods
- Single linkage
- Complete linkage
- Average linkage
ii. Variance methods (Ward’s method)
iii. Centroid methods
b. Divisive
Nonhierarchical
a. Sequential threshold
b. Parallel threshold
c. Optimizing partitioning

7. Cluster Analysis Process
Decide on number of Clusters
The methods are:
Theoretical, conceptual or practical considerations
Hierarchical clustering
Nonhierarchical clustering
The relative sizes of the clusters should be meaningful

8. Cluster Analysis Process
Interpret and profile the clusters
It involves examining the cluster centroids. The centroids represent the mean values of the objects contained in the cluster on each of the variables.
The variables that significantly differentiate between clusters can be identified via discriminant analysis and one way analysis of variance.

9. Cluster Analysis Process
Asses reliability and validity
Perform cluster analysis on the same data using different distance measure.
Use different methods of clustering and compare the results.
Split the data randomly into halves.
Delete variables randomly.

10. Cluster Analysis Process: SPSS Windows
Finding Agglomeration Schedule, Cluster Member and ICICLE Plot
Analyze > Classify > Hierarchical Cluster
Statistics > Check on Agglomeration schedule and Check on Range of solutions > 2-4 (for 2-4 clusters)
Plots > check on dendogram
Method > Clusters method > ward’s method OK