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

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

Multivariate Data Analysis

Chapter 9 - Cluster Analysis

Section 3: Independence Techniques

Page 2: 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)

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

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

Page 4: Multivariate Data Analysis Chapter 9 - Cluster Analysis Section 3: Independence Techniques

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)

Page 5: Multivariate Data Analysis Chapter 9 - Cluster Analysis Section 3: Independence Techniques

Cluster Analysis Decision Process (Cont.)

Stage 3: Assumptions in Cluster Analysis Representativeness of the Sample Impact of Multicollinearity

Page 6: Multivariate Data Analysis Chapter 9 - Cluster Analysis Section 3: Independence Techniques

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

Page 7: Multivariate Data Analysis Chapter 9 - Cluster Analysis Section 3: Independence Techniques

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

Page 8: Multivariate Data Analysis Chapter 9 - Cluster Analysis Section 3: Independence Techniques

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

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

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