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

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)

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

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)

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

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

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

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

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