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Cluster Analysis, 5th Edition

Book Description

Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics.

This fifth edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data.

Real life examples are used throughout to demonstrate the application of the theory, and figures are used extensively to illustrate graphical techniques. The book is comprehensive yet relatively non-mathematical, focusing on the practical aspects of cluster analysis.

Key Features:

  • Presents a comprehensive guide to clustering techniques, with focus on the practical aspects of cluster analysis.

  • Provides a thorough revision of the fourth edition, including new developments in clustering longitudinal data and examples from bioinformatics and gene studies

  • Updates the chapter on mixture models to include recent developments and presents a new chapter on mixture modeling for structured data.

Practitioners and researchers working in cluster analysis and data analysis will benefit from this book.

Table of Contents

  1. Cover
  2. Wiley Series in Probability and Statistics
  3. Title Page
  4. Copyright
  5. Dedication
  6. Preface
  7. Acknowledgement
  8. Chapter 1: An Introduction to classification and clustering
    1. 1.1 Introduction
    2. 1.2 Reasons for Classifying
    3. 1.3 Numerical Methods of Classification – Cluster Analysis
    4. 1.4 What is a Cluster?
    5. 1.5 Examples of the Use of Clustering
    6. 1.6 Summary
  9. Chapter 2: Detecting clusters graphically
    1. 2.1 Introduction
    2. 2.2 Detecting Clusters with Univariate and Bivariate Plots of Data
    3. 2.3 Using Lower-Dimensional Projections of Multivariate Data for Graphical Representations
    4. 2.4 Three-dimensional Plots and Trellis Graphics
    5. 2.5 Summary
  10. Chapter 3: Measurement of proximity
    1. 3.1 Introduction
    2. 3.2 Similarity Measures for Categorical Data
    3. 3.3 Dissimilarity and Distance Measures for Continuous Data
    4. 3.4 Similarity Measures for Data Containing both Continuous and Categorical Variables
    5. 3.5 Proximity Measures for Structured Data
    6. 3.6 Inter-group Proximity Measures
    7. 3.7 Weighting Variables
    8. 3.8 Standardization
    9. 3.9 Choice of Proximity Measure
    10. 3.10 Summary
  11. Chapter 4: Hierarchical clustering
    1. 4.1 Introduction
    2. 4.2 Agglomerative Methods
    3. 4.3 Divisive Methods
    4. 4.4 Applying the Hierarchical Clustering Process
    5. 4.5 Applications of Hierarchical Methods
    6. 4.6 Summary
  12. Chapter 5: Optimization clustering techniques
    1. 5.1 Introduction
    2. 5.2 Clustering Criteria Derived from the Dissimilarity Matrix
    3. 5.3 Clustering Criteria Derived from Continuous Data
    4. 5.4 Optimization Algorithms
    5. 5.5 Choosing the Number of Clusters
    6. 5.6 Applications of Optimization Methods
    7. 5.7 Summary
  13. Chapter 6: Finite mixture densities as models for cluster analysis
    1. 6.1 Introduction
    2. 6.2 Finite Mixture Densities
    3. 6.3 Other Finite Mixture Densities
    4. 6.4 Bayesian Analysis of Mixtures
    5. 6.5 Inference for Mixture Models with Unknown Number of Components and Model Structure
    6. 6.6 Dimension Reduction – Variable Selection in Finite Mixture Modelling
    7. 6.7 Finite Regression Mixtures
    8. 6.8 Software for Finite Mixture Modelling
    9. 6.9 Some Examples of the Application of Finite Mixture Densities
    10. 6.10 Summary
  14. Chapter 7: Model-based cluster analysis for structured data
    1. 7.1 Introduction
    2. 7.2 Finite Mixture Models for Structured Data
    3. 7.3 Finite Mixtures of Factor Models
    4. 7.4 Finite Mixtures of Longitudinal Models
    5. 7.5 Applications of Finite Mixture Models for Structured Data
    6. 7.6 Summary
  15. Chapter 8: Miscellaneous clustering methods
    1. 8.1 Introduction
    2. 8.2 Density Search Clustering Techniques
    3. 8.3 Density-based Spatial Clustering of Applications with Noise
    4. 8.4 Techniques Which Allow Overlapping Clusters
    5. 8.5 Simultaneous Clustering of Objects and Variables
    6. 8.6 Clustering with Constraints
    7. 8.7 Fuzzy Clustering
    8. 8.8 Clustering and Artificial Neural Networks
    9. 8.9 Summary
  16. Chapter 9: Some final comments and guidelines
    1. 9.1 Introduction
    2. 9.2 Using Clustering Techniques in Practice
    3. 9.3 Testing for Absence of Structure
    4. 9.4 Methods for Comparing Cluster Solutions
    5. 9.5 Internal Cluster Quality, Influence and Robustness
    6. 9.6 Displaying Cluster Solutions Graphically
    7. 9.7 Illustrative Examples
    8. 9.8 Summary
  17. References
  18. Index