Cluster Analysis, 5th Edition
Brian S. Everitt, Sabine Landau, Morven Leese, Daniel Stahl(auth.), Walter A. Shewhart, Samuel S. Wilks(eds.) 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.Content:
Chapter 1 An Introduction to Classification and Clustering (pages 1–13):
Chapter 2 Detecting Clusters Graphically (pages 15–41):
Chapter 3 Measurement of Proximity (pages 43–69):
Chapter 4 Hierarchical Clustering (pages 71–110):
Chapter 5 Optimization Clustering Techniques (pages 111–142):
Chapter 6 Finite Mixture Densities as Models for Cluster Analysis (pages 143–186):
Chapter 7 Model?Based Cluster Analysis for Structured Data (pages 187–213):
Chapter 8 Miscellaneous Clustering Methods (pages 215–255):
Chapter 9 Some Final Comments and Guidelines (pages 257–287):