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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