Distances and Similarities in Intuitionistic Fuzzy Sets

• Κύριος συγγραφέας: Szmidt, Eulalia (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2014.
• Σειρά: Studies in Fuzziness and Soft Computing, 307
• Θέματα: Engineering.; Artificial intelligence.; Operations research.; Management science.; Computational intelligence.; Computational Intelligence.; Operations Research, Management Science.; Artificial Intelligence (incl. Robotics).
• Φυσική περιγραφή: VIII, 148 p. online resource.
• ISBN: 9783319016405
• ISSN: 1434-9922 ;

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.