Cluster Analysis

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Taylor & Francis, 2001 - Mathematics - 237 pages
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Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organising multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques are applicable in a wide range of areas such as medicine, psychology and market research. This fourth edition of the highly successful Cluster Analysis represents a thorough revision of the third edition and covers new and developing areas such as classification likelihood and neural networks for clustering. 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.
 

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This book is an in depth presentation of clustering. Concepts are explained well. There aren't many books devoted entirely to cluster analysis, but this is the best of those I have seen.

Contents

Optimization Clustering Techniques
90
Finite Mixture Densities as Models for Cluster Analysis
118
Miscellaneous Clustering Methods
141
Some Final Comments and Guidelines
177
Software for Cluster Analysis
197
Bibliography
208
Index
231
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About the author (2001)

Brian S. Everitt, Sabine Landau and Morven Leese are all at the Institute of Psychiatry, King's College London.