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Distances and Similarities in Intuitionistic Fuzzy Sets [electronic resource] / by Eulalia Szmidt.

By: Contributor(s): Material type: TextTextSeries: Studies in Fuzziness and Soft Computing ; 307Publisher: Cham : Springer International Publishing : Imprint: Springer, 2014Description: VIII, 148 p. 35 illus., 17 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783319016405
Subject(s): Additional physical formats: Printed edition:: No titleDDC classification:
  • 006.3 23
LOC classification:
  • Q342
Online resources:
Contents:
Intuitionistic Fuzzy Sets as a Generalization of Fuzzy Sets -- Distances -- Similarity Measures between Intuitionistic Fuzzy Sets.
In: Springer eBooksSummary: 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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E-Book E-Book Central Library Available E-41196

Intuitionistic Fuzzy Sets as a Generalization of Fuzzy Sets -- Distances -- Similarity Measures between 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, 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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