Clustering for Data Mining_ A Data Recovery Approach [Mirkin 2005-04-29].pdf

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Computer Science and Data Analysis Series
Clustering for
Data Mining
A Data Recovery Approach
Boris Mirkin
Boca Raton London New York Singapore
© 2005
by Taylor & Francis Group, LLC
Published in 2005 by
Chapman & Hall/CRC
Taylor & Francis Group
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Boca Raton, FL 33487-2742
© 2005 by Taylor & Francis Group, LLC
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Printed in the United States of America on acid-free paper
10 9 8 7 6 5 4 3 2 1
International Standard Book Number-10: 1-58488-534-3 (Hardcover)
International Standard Book Number-13: 978-1-58488-534-4 (Hardcover)
Library of Congress Card Number 2005041421
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Library of Congress Cataloging-in-Publication Data
Mirkin, B. G. (Boris Grigorévich)
Clustering for data mining : a data recovery approach / Boris Mirkin.
p. cm. -- (Computer science and data analysis series ; 3)
Includes bibliographical references and index.
ISBN 1-58488-534-3
1. Data mining. 2. Cluster analysis. I. Title. II. Series.
QA76.9.D343M57 2005
006.3'12--dc22
2005041421
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© 2005
by Taylor & Francis Group, LLC
Chapman & Hall/CRC
Computer Science and Data Analysis Series
The interface between the computer and statistical sciences is increasing,
as each discipline seeks to harness the power and resources of the other.
This series aims to foster the integration between the computer sciences
and statistical, numerical, and probabilistic methods by publishing a broad
range of reference works, textbooks, and handbooks.
SERIES EDITORS
John Lafferty, Carnegie Mellon University
David Madigan, Rutgers University
Fionn Murtagh, Royal Holloway, University of London
Padhraic Smyth, University of California, Irvine
Proposals for the series should be sent directly to one of the series editors
above, or submitted to:
Chapman & Hall/CRC
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Published Titles
Bayesian Artificial Intelligence
Kevin B. Korb and Ann E. Nicholson
Pattern Recognition Algorithms for Data Mining
Sankar K. Pal and Pabitra Mitra
Exploratory Data Analysis with MATLAB
®
Wendy L. Martinez and Angel R. Martinez
Clustering for Data Mining: A Data Recovery Approach
Boris Mirkin
Correspondence Analysis and Data Coding with JAVA and R
Fionn Murtagh
R Graphics
Paul Murrell
© 2005
by Taylor & Francis Group, LLC
Contents
Preface
List of Denotations
Introduction: Historical Remarks
1 What Is Clustering
Base words
1.1 Exemplary problems
1.1.1 Structuring
1.1.2 Description
1.1.3 Association
1.1.4 Generalization
1.1.5 Visualization of data structure
1.2 Bird's-eye view
1.2.1 De nition: data and cluster structure
1.2.2 Criteria for revealing a cluster structure
1.2.3 Three types of cluster description
1.2.4 Stages of a clustering application
1.2.5 Clustering and other disciplines
1.2.6 Di erent perspectives of clustering
Base words
2.1 Feature characteristics
2.1.1 Feature scale types
2.1.2 Quantitative case
2.1.3 Categorical case
2.2 Bivariate analysis
2.2.1 Two quantitative variables
2.2.2 Nominal and quantitative variables
2 What Is Data
© 2005
by Taylor & Francis Group, LLC

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