Cluster analysis

Table of Contents:

1 An Introduction to classification and clustering.
2 Detecting clusters graphically.
3 Measurement of proximity.
4 Hierarchical clustering.
5 Optimization clustering techniques.
6 Finite mixture densities as models for cluster analysis.
7 Model-based cluster analysis for structured data.
8 Miscellaneous clustering methods.
9 Some final comments and guidelines.

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.

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