Distances and Similarities in Intuitionistic Fuzzy Sets

  • Eulalia Szmidt

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 307)

Table of contents

  1. Front Matter
    Pages 1-6
  2. Eulalia Szmidt
    Pages 1-5
  3. Eulalia Szmidt
    Pages 39-85
  4. Eulalia Szmidt
    Pages 131-132
  5. Back Matter
    Pages 133-147

About this book


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.


Big Databases Decision-Making Hausdorff Distance Individual Preference Interval-Valued Fuzzy Sets Mass Assignment Theory Measure of Consensus Pearson´s Correlation Coefficient

Authors and affiliations

  • Eulalia Szmidt
    • 1
  1. 1.Polish Academy of SciencesSystems Research InstituteWarsawPoland

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-01640-5
  • Copyright Information Springer International Publishing Switzerland 2014
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-01639-9
  • Online ISBN 978-3-319-01640-5
  • Series Print ISSN 1434-9922
  • Series Online ISSN 1860-0808
  • About this book