On the J-divergence of intuitionistic fuzzy sets with its application to pattern recognition

  • Authors:

  • Wen-Liang Hung;Miin-Shen Yang

  • Affiliations:

  • Graduate Institute of Computer Science, National Hsinchu University of Education, Hsin-Chu, Taiwan;Department of Applied Mathematics, Chung Yuan Christian University, Chung-Li, Taiwan

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2008

Quantified Score

Hi-index 0.08

Visualization

Abstract

The importance of suitable distance measures between intuitionistic fuzzy sets (IFSs) arises because of the role they play in the inference problem. A concept closely related to one of distance measures is a divergence measure based on the idea of information-theoretic entropy that was first introduced in communication theory by Shannon (1949). It is known that J-divergence is an important family of divergences. In this paper, we construct J-divergence between IFSs. The proposed J-divergence can induce some useful distance and similarity measures between IFSs. Numerical examples demonstrate that the proposed measures perform well in clustering and pattern recognition.