Ebook: Distances and Similarities in Intuitionistic Fuzzy Sets
Author: Eulalia Szmidt (auth.)
- Tags: Computational Intelligence, Operations Research Management Science, Artificial Intelligence (incl. Robotics)
- Series: Studies in Fuzziness and Soft Computing 307
- Year: 2014
- Publisher: Springer International Publishing
- Edition: 1
- Language: English
- pdf
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.
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.
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:
Front Matter....Pages 1-6
Introduction....Pages 1-5
Intuitionistic Fuzzy Sets as a Generalization of Fuzzy Sets....Pages 7-38
Distances....Pages 39-85
Similarity Measures between Intuitionistic Fuzzy Sets....Pages 87-129
Summary and Conclusions....Pages 131-132
Back Matter....Pages 133-147