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Multivariate Data Analysis Chapter 9 - Cluster Analysis

Multivariate Data Analysis Chapter 9 - Cluster Analysis. MIS 6093 Statistical Method Instructor: Dr. Ahmad Syamil. Chapter 9. What Is Cluster Analysis? How Does Cluster Analysis Work? Measuring Similarity Forming Clusters Determining the Number of Clusters in the Final Solution .

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Multivariate Data Analysis Chapter 9 - Cluster Analysis

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  1. Multivariate Data AnalysisChapter 9 - Cluster Analysis MIS 6093 Statistical Method Instructor: Dr. Ahmad Syamil

  2. Chapter 9 • What Is Cluster Analysis? • How Does Cluster Analysis Work? • Measuring Similarity • Forming Clusters • Determining the Number of Clusters in the Final Solution

  3. Chapter 9Cluster Analysis Decision Process • Stage One: Objectives of Cluster Analysis • Selection of Clustering Variables

  4. Chapter 9Cluster Analysis Decision Process Cont. • Stage 2: Research Design in Cluster Analysis • Detecting Outliers • Similarity Measures • Correlational Measures • Distance Measures • Comparison to Correlational Measures • Types of Distance Measures • Impact of Unstandardized Data Values • Association Measures • Standardizing the Data • Standardizing By Variables • Standardizing By Observation

  5. Chapter 9Cluster Analysis Decision Process Cont. • Stage 3: Assumptions in Cluster Analysis • Representativeness of the Sample • Impact of Multicollinearity

  6. Chapter 9Cluster 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. Chapter 9Cluster Analysis Decision Process Cont. • Stage 5: Interpretation of the Clusters • Stage 6: Validation and Profiling of the Clusters • Validating the Cluster Solution • Profiling the Cluster Solution • Summary of the Decision Process

  8. Chapter 9An Illustrative Example • Stage 1: Objectives of the Cluster Analysis • Stage 2: Research Design of the Cluster Analysis • Stage 3: Assumptions in Cluster Analysis

  9. Chapter 9An 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 • Stage 6: Validation and Profiling of the Clusters

  10. Chapter 9 • Summary • Questions ……end

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