multivariate data analysis chapter 9 - cluster analysis section 3: independence techniques
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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