Similarity and Compatibility in Fuzzy Set Theory – Assessment and Applications

Citation

Mann, C.J.H. (2002), "Similarity and Compatibility in Fuzzy Set Theory – Assessment and Applications", Kybernetes, Vol. 31 No. 6. https://doi.org/10.1108/k.2002.06731fae.001

Similarity and Compatibility in Fuzzy Set Theory – Assessment and Applications

V.V. Cross and T.A. SudkampPhysica-VerlagHeidelberg2002xi + 209 pp. 27 Figs., 46 TablesISBN 3-7908-1458-Xhardcover, EUR 54.95 SFR 91.00 GBP£38.50US $72.00

Keywords: Publication, Cybernetics, Fuzzy Sets

This is another book in the series edited by J. Kacprzyk called: Studies in Fuzziness and Soft Computing -Volume 93. The authors of this contribution are Valerie V. Cross of the University of South Carolina, SC, USA and Thomas A. Sudkamp of Wright State University, Dayton OH, USA.

Assessing the degree to which two objects, an object and a query, or two concepts are similar or compatible is a fundamental component of human reasoning and consequently is critical in the development of automated diagnosis, classification, information retrieval and decision systems. The assessment of similarity has played an important role in such diverse disciplines such as taxonomy, psychology, and the social sciences. Each discipline has proposed methods for quantifying similarity judgments suitable for its particular applications. This book presents a unified approach to quantifying similarity and compatibility within the framework of fuzzy set theory and examines the primary importance of these concepts in approximate reasoning. Examples of the application of similarity measures in various areas including expert systems, information retrieval, and intelligent database systems are provided.

Part I. Similarity, Compatibility, and Fuzzy Set Theory: The Nature of Similarity; Historic Assessment of Compatibility; Foundations of Fuzzy Set Theory; Compatibility in Fuzzy Inference; Compatibility in Approximate Reasoning.

Part II. Taxonomy of Compatibility Measures: Set-Theoretic Measures; Proximity-Based Measures; Logic-Based Measures; Fuzzy-Valued Similarity Measures.

Part III. Empirical Analysis of Compatibility Measures: Generic Classification Domain; Set- Theoretic Comparative Study; Proximity-Based Comparative Study; Logic-Based Comparative Study; Comparison Among the Three Classes.- References.

Internet: http://www.springer.de/cgi-bin/search_book.pl?isbn=3-7908-1458-X