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Distances and Similarities in Intuitionistic Fuzzy Sets [libro electrónico] / by Eulalia Szmidt.

Por: Tipo de material: TextoTextoSeries Detalles de publicación: Cham : Springer International Publishing : Imprint: Springer, 2014.Descripción: viii, 148 pTipo de contenido:
  • text
Tipo de medio:
  • computer
Tipo de soporte:
  • online resource
ISBN:
  • 9783319016405
Tema(s): Formatos físicos adicionales: Printed edition:: Sin títuloClasificación LoC:
  • Q342
Recursos en línea:
Contenidos:
Intuitionistic Fuzzy Sets as a Generalization of Fuzzy Sets -- Distances -- Similarity Measures between Intuitionistic Fuzzy Sets.
Resumen: 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.
Tipo de ítem: Libro electrónico Lista(s) en las que aparece este ítem: Ebooks
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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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