1. Multivariate Data Analysis Chapter 9 - Cluster Analysis
Section 3: Independence Techniques

2. Chapter 9 What Is Cluster Analysis (Q analysis)?
Define groups of homogeneous objects (i.e., individuals, firms, products, or behaviors)
Maximize the homogeneity of objects within the clusters while also maximize the heterogeneity between clusters
Segmentation and target marketing
Compare with Factor Analysis
How Does Cluster Analysis Work?
Measuring Similarity (Euclidean distance)
Forming Clusters (hierarchical procedure vs. agglomerative method)
Determining the Number of Clusters in the Final Solution (entropy group)

3. Cluster Analysis Decision Process
Stage One: Objectives of Cluster Analysis
Taxonomy description
Data simplification
Relationship identification
Selection of Clustering Variables
Characterize the objects being clustered
Relate specifically to the objectives of the cluster analysis

4. Cluster Analysis Decision Process (Cont.)
Stage 2: Research Design in Cluster Analysis
Detecting Outliers
Similarity Measures (Interobject similarity)
Correlational Measures
Distance Measures
Comparison to Correlational Measures
Types of Distance Measures (Euclidean distance)
Impact of Unstandardized Data Values (Mahalonobis Distance, D2)
Association Measures
Standardizing the Data
Standardizing By Variables (normalized distance function)
Standardizing By Observation (within-case vs. row-centering standarlization)

5. Cluster Analysis Decision Process (Cont.)
Stage 3: Assumptions in Cluster Analysis
Representativeness of the Sample
Impact of Multicollinearity

6. Cluster Analysis Decision Process (Cont.)
Stage 4: Deriving Clusters and Assessing Overall Fit
Clustering Algorithms
Hierarchical Cluster Procedures
Single Linkage
Complete Linkage
Average Linkage
Ward's Method
Centroid Method
Nonhierarchical Clustering Procedures
Sequential Threshold
Parallel Threshold
Optimization
Selecting Seed Points
Should Hierarchical or Nonhierarchical Methods Be Used?
Pros and Cons of Hierarchical Methods
Emergence of Nonhierarchical Methods
A Combination of Both Methods
How Many Clusters Should Be Formed?
Should the Cluster Analysis Be Respecified

7. Cluster Analysis Decision Process (Cont.)
Stage 5: Interpretation of the Clusters
Stage 6: Validation and Profiling of the Clusters
Validating the Cluster Solution
Criterion or predictive validity
Profiling the Cluster Solution
Summary of the Decision Process

8. An Illustrative Example
Stage 1: Objectives of the Cluster Analysis
Segment objects (customers) into groups with similar perceptions of HATCO
HATCO can then formulate strategies with different appeals for the separate groups.
Stage 2: Research Design of the Cluster
Analysis
Identify any outliers
Similarity measure (multicollinearity: D2)
Stage 3: Assumptions in Cluster Analysis

9. An Illustrative Example (Cont.)
Stage 4: Deriving Clusters and Assessing
Overall Fit
Step 1: Hierarchical Cluster Analysis
Step 2: Nonhierarchical Cluster Analysis
Stage 5: Interpretation of the Clusters
Two-cluster solution
Four-cluster solution
Stage 6: Validation and Profiling of the Clusters
Managerial view