| 书目名称 | Distances and Similarities in Intuitionistic Fuzzy Sets |
| 编辑 | Eulalia Szmidt |
| 视频video | |
| 概述 | 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 |
| 丛书名称 | Studies in Fuzziness and Soft Computing |
| 图书封面 |  |
| 描述 | .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.. |
| 出版日期 | Book 2014 |
| 关键词 | Big Databases; Decision-Making; Hausdorff Distance; Individual Preference; Interval-Valued Fuzzy Sets; Ma |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-01640-5 |
| isbn_softcover | 978-3-319-03302-0 |
| isbn_ebook | 978-3-319-01640-5Series ISSN 1434-9922 Series E-ISSN 1860-0808 |
| issn_series | 1434-9922 |
| copyright | Springer International Publishing Switzerland 2014 |
发表于 2025-3-21 18:20:57
