Some results on information measures for complex intuitionistic fuzzy sets

Complex intuitionistic fuzzy sets (CIFSs), modeled by complex‐valued membership and nonmembership functions with codomain the unit disc in a complex plane, handle two‐dimensional information in a single set. Under this environment, the primary objective of the present study is to introduce some nove...

Full description

Saved in:
Bibliographic Details
Published inInternational journal of intelligent systems Vol. 34; no. 10; pp. 2319 - 2363
Main Authors Garg, Harish, Rani, Dimple
Format Journal Article
LanguageEnglish
Published New York Hindawi Limited 01.10.2019
Subjects
Algorithms
Clustering
Comparative analysis
complex fuzzy set
complex intuitionistic fuzzy set
decision‐making
Distance measurement
Fuzzy sets
information measures
Intelligent systems
Multiple criterion
Pattern recognition
Similarity
Similarity measures
Online AccessGet full text

Cover

More Information
Summary:Complex intuitionistic fuzzy sets (CIFSs), modeled by complex‐valued membership and nonmembership functions with codomain the unit disc in a complex plane, handle two‐dimensional information in a single set. Under this environment, the primary objective of the present study is to introduce some novel formulae of information measures (similarity measures, distance measures, entropies, and inclusion measures) and discuss the transformation relationships among them. To demonstrate the efficiency of the proposed similarity measures, we apply it to pattern recognition problem and a detailed comparative analysis is conducted with some of the existing measures. Further, algorithms based on proposed measures are developed for handing multicriteria decision‐making problems and their working is illustrated with the help of an example. Besides this, the practicality of the proposed similarity measure is demonstrated by developing a clustering algorithm under CIFS environment.
ISSN:0884-8173
1098-111X
DOI:10.1002/int.22127