Logo - springer
Slogan - springer

Engineering - Computational Intelligence and Complexity | Distances and Similarities in Intuitionistic Fuzzy Sets

Distances and Similarities in Intuitionistic Fuzzy Sets

Szmidt, Eulalia

2014, VIII, 148 p. 35 illus., 17 illus. in color.

Available Formats:

Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.

You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.

After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.


(net) price for USA

ISBN 978-3-319-01640-5

digitally watermarked, no DRM

Included Format: PDF

download immediately after purchase

learn more about Springer eBooks

add to marked items


Hardcover version

You can pay for Springer Books with Visa, Mastercard, American Express or Paypal.

Standard shipping is free of charge for individual customers.


(net) price for USA

ISBN 978-3-319-01639-9

free shipping for individuals worldwide

usually dispatched within 3 to 5 business days

add to marked items

  • 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

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 Level » Research

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

Related subjects » Applications - Artificial Intelligence - Computational Intelligence and Complexity

Table of contents 

Intuitionistic Fuzzy Sets as a Generalization of Fuzzy Sets.- Distances.- Similarity Measures between Intuitionistic Fuzzy Sets.

Popular Content within this publication 



Read this Book on Springerlink

Services for this book

New Book Alert

Get alerted on new Springer publications in the subject area of Computational Intelligence.