Finding Groups in Data

An Introduction to Cluster Analysis

Leonard Kaufman, Peter J. Rousseeuw - Collection Wiley Series in Probability and Statistics

342 pages, parution le 20/05/2005

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Résumé

An introduction to the practical application of cluster analysis, Finding Groups in Data presents a selection of methods that together can deal with most applications. These methods are chosen for their robustness, consistency, and general applicability. The text discusses the main approaches to clustering and provides guidance in choosing between the available methods. It also discusses various types of data, including interval-scaled and binary variables as well as similarity data and explains how these can be transformed prior to clustering. With numerous exercises to aid learning, Finding Groups in Data provides an invaluable introduction to cluster analysis with an emphasis on methods that are both easy to use and modern.

L'auteur - Leonard Kaufman

Leonard Kaufman, PhD, is affiliated with Vrije University in Brussels, Belgium.

L'auteur - Peter J. Rousseeuw

Peter J. Rousseeuw, PhD, is Professor in the Department of Mathematics and Computer Science at the University of Antwerp in Belgium.

Sommaire

Introduction
Partitioning Around Medoids (Program PAM)
Clustering large Applications (Program CLARA)
Fuzzy Analysis
Agglomerative Nesting (Program AGNES)
Divisive Analysis (Program DIANA)
Monothetic Analysis (Program MONA)
Appendix 1: Implementation and Structure of the Programs
Appendix 2: Running the Programs
Appendix 3: Adapting the Programs to Your Needs
Appendix 4: The Program CLUSPLOT
References
Author Index
Subject Index

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Wiley
Auteur(s)	Leonard Kaufman, Peter J. Rousseeuw
Collection	Wiley Series in Probability and Statistics
Parution	20/05/2005
Nb. de pages	342
Format	15,5 x 23,5
Couverture	Broché
Poids	481g
Intérieur	Noir et Blanc
EAN13	9780471735786
ISBN13	978-0-471-73578-6