Springer Distances and Similarities in Intuitionistic Fuzzy Sets: 307 (Studies in Fuzziness and Soft Computing, 307)

This book provides 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. Review From the book reviews:“This research monograph is dedicated to a detailed study of distances and similarities in the theory of intuitionistic fuzzy sets. … The monograph includes numerous examples and figures that facilitate the reader’s understanding. … The book is an important contribution to the literature on intuitionistic fuzzy sets, being a comprehensive tool in the introduction and handling of distances and similarities between intuitionistic fuzzy sets.” (Adrian Ioan Ban, Mathematical Reviews, August, 2014) From the Back Cover 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.