A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications
The present book outlines a new approach to possibilistic clustering in which the sought clustering structure of the set of objects is based directly on the formal definition of fuzzy cluster and the possibilistic memberships are determined directly from
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Dmitri A. Viattchenin
A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications
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Studies in Fuzziness and Soft Computing Editor-in-Chief Prof. Janusz Kacprzyk Systems Research Institute Polish Academy of Sciences ul. Newelska 6 01-447 Warsaw Poland E-mail: [email protected]
For further volumes: http://www.springer.com/series/2941
297
Dmitri A. Viattchenin
A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications
ABC
Dmitri A. Viattchenin Laboratory of Information Protection United Institute of Informatics Problems National Academy of Sciences of Belarus Minsk Belarus
ISSN 1434-9922 ISBN 978-3-642-35535-6 DOI 10.1007/978-3-642-35536-3
ISSN 1860-0808 (electronic) ISBN 978-3-642-35536-3 (eBook)
Springer Heidelberg New York Dordrecht London Library of Congress Control Number: 2012954481 c Springer-Verlag Berlin Heidelberg 2013 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
To my parents
Foreword
Clustering has emerged many decades ago and has formed a fundamental framework supporting various pursuits of data analysis: finding structures in data, determining associations, discovering similarities in time series and spatiotemporal data, building prediction rules, and forming classifiers. Applications o
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