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/11080008
Hidden Bibliographic Details
ISBN:9783319016405
3319016407
3319016393
9783319016399
9783319016399
Notes:Includes bibliographical references and index.
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.
Other form:Printed edition: 9783319016399
Standard no.:10.1007/978-3-319-01640-5

MARC

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505 0 0 |t Intuitionistic Fuzzy Sets as a Generalization of Fuzzy Sets --  |t Distances --  |t Similarity Measures between Intuitionistic Fuzzy Sets. 
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. 
504 |a Includes bibliographical references and index. 
588 0 |a Online resource; title from PDF title page (SpringerLink, viewed August 20, 2013). 
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