Distances and similarities in intuitionistic fuzzy sets /

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Bibliographic Details
Author / Creator:Szmidt, Eulalia. author.
Imprint:Cham : Springer, [2014]
Description:1 online resource (viii, 148 pages) : illustrations.
Language:English
Series:Studies in fuzziness and soft computing, 1434-9922 ; 307
Studies in fuzziness and soft computing ; 307.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/9853147
Hidden Bibliographic Details
ISBN:9783319016405 (electronic bk.)
3319016407 (electronic bk.)
9783319016399
Notes:Includes bibliographical references and index.
Description based on online resource; title from PDF title page (SpringerLink, viewed August 20, 2013).
Summary: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, decision making, and control. The work provides readers with a comprehensive set of theoretical concepts and practical tools for both defining and determining similarity between intuitionistic fuzzy sets. It describes an automatic algorithm for deriving intuitionistic fuzzy sets from data, which can aid in the analysis of information in large databases. The book also discusses other important applications, e.g. the use of similarity measures to evaluate the extent of agreement between experts in the context of decision making.
Standard no.:10.1007/978-3-319-01640-5

MARC

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520 |a 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, decision making, and control. The work provides readers with a comprehensive set of theoretical concepts and practical tools for both defining and determining similarity between intuitionistic fuzzy sets. It describes an automatic algorithm for deriving intuitionistic fuzzy sets from data, which can aid in the analysis of information in large databases. The book also discusses other important applications, e.g. the use of similarity measures to evaluate the extent of agreement between experts in the context of decision making. 
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