Cluster analysis comprises a range of methods of classifying multivariate data into subgroups, and these techniques are widely applicable. The 4th edition of Cluster Analysis updates the successful 3rd edition and incorporates new material to cover developing areas, such as Bayesian statistics and neural networks. In addition, recent research on the evaluation of results and the assessment of the number of clusters has also been included. Real-life examples are used throughout to demonstrate the application of the theory, and graphical techniques are demonstrated with appropriate figures. Finally, this edition includes information on the software packages currently available. The author assumes some prior knowledge of statistics, but writes in a non-mathematical, accessible style. This concise book is ideal for postgraduate students of statistics, as well as researchers in medicine, sociology, and market research.
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Brian S. Everitt, Sabine Landau and Morven Leese are all at the Institute of Psychiatry, King's College London.
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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