Distances and Similarities in Intuitionistic Fuzzy Sets
This book presents the state-of-the-art in theory and practice regarding similarity and distance measures for intuitionistic fuzzy sets. Quantifying similarity and distances is crucial for many applications, e.g. data mining, machine learning, decisi
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Eulalia Szmidt
Distances and Similarities in Intuitionistic Fuzzy Sets
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
307
Eulalia Szmidt
Distances and Similarities in Intuitionistic Fuzzy Sets
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Eulalia Szmidt Systems Research Institute Polish Academy of Sciences Warsaw Poland
ISSN 1434-9922 ISSN 1860-0808 (electronic) ISBN 978-3-319-01639-9 ISBN 978-3-319-01640-5 (eBook) DOI 10.1007/978-3-319-01640-5 Springer Cham Heidelberg New York Dordrecht London Library of Congress Control Number: 2001012345 c Springer International Publishing Switzerland 2014 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)
Foreword
The book I am glad to write my foreword to is a very relevant position in literature on intuitionistic fuzzy sets or, maybe even more generally, in the fuzzy set theory. In virtually all application issues related to the very essence of similarity, distances are crucial. Just to quote some more important examples, let me mention data analysis and data mining, machine learning, decision theory and analysis, control etc. Of course, this short list
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