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State-of-the-art theory and practice in similarity and distance measures for intuitionistic fuzzy sets
Includes new definitions and computational algorithms
Written by an expert in the field
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.
Content Level »Research
Keywords »Big Databases - Decision-Making - Hausdorff Distance - Individual Preference - Interval-Valued Fuzzy Sets - Mass Assignment Theory - Measure of Consensus - Pearson´s Correlation Coefficient