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Multivariate Data Analysis Chapter 9 - Cluster Analysis

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)

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Multivariate Data Analysis Chapter 9 - Cluster Analysis

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  1. Multivariate Data AnalysisChapter 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

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