Similarity measure of intuitionistic fuzzy numbers and its application to clustering

Satyajit Das and Debashree Guha

International Journal of Mathematics in Operational Research, 2017, vol. 10, issue 4, 399-430

Abstract: The aim of this study is threefold. First of all, a novel method to calculate the degree of similarity between intuitionistic fuzzy numbers (IFNs) by using the concept of centroid point of IFNs is presented. Secondly, in order to compare the proposed method with the existing similarity measures some examples are demonstrated. The numerical results show that the new similarity measure can overcome the limitations of the existing methods and thus, the proposed similarity measure is more reasonable and effective from application point of view. Finally, a clustering algorithm, in which data are quantified by IFNs, is introduced by utilising the proposed similarity measure. In addition, a pattern recognition problem is demonstrated to illustrate the practicality and effectiveness of this similarity-based clustering technique.

Keywords: intuitionistic fuzzy number; centroid point; similarity measure; clustering; pattern recognition. (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=84157 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijmore:v:10:y:2017:i:4:p:399-430

Access Statistics for this article

More articles in International Journal of Mathematics in Operational Research from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().