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Handbook of Cluster Analysis (Hardcover)

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After reviewing the history of cluster analysis, this collection describes the four primary approaches to cluster analysis: optimization, dissimilarity-based methods, probability models, and density estimation. The main focus of the handbook is on structures following the partitioning paradigm in some sense, that is, partitions, hierarchies, and probabilistic clusterings. The 31 contributions describe center-based clustering, mixture models for standard p=dimensional Euclidean data, latent class models for categorical data, the Dirichlet process, clustering of symbolic data, significance testing, two-mode partitioning, and method-independent indices for cluster validation. Annotation ©2016 Ringgold, Inc., Portland, OR (protoview.com)

The application of cluster analysis (collecting entities that belong together in some sense in a class) is widespread in engineering, medicine, biology, social science, and many other areas. This handbook provides a comprehensive overview of cluster analysis methodology and applications. It summarizes all the recent research in one volume. The book helps researchers understand the state of the art in the field, how to apply cluster analysis using existing methods, and how to choose a method and evaluate the result.

Number of Pages: 753
Genre: Business + Money Management, Mathematics, Computers + Internet
Sub-Genre: Machine Theory, Statistics, Probability + Statistics / General
Series Title: Chapman & Hall/CRC Handbooks of Modern Statistical Methods
Format: Hardcover
Publisher: Taylor & Francis
Language: English
Street Date: December 1, 2015
TCIN: 15683164
UPC: 9781466551886
Item Number (DPCI): 248-87-8486
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