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A dissimilarity Jensen–Shannon divergence measure for intuitionistic fuzzy sets
Author(s) -
Joshi Rajesh,
Kumar Satish
Publication year - 2018
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.22026
Subject(s) - measure (data warehouse) , divergence (linguistics) , entropy (arrow of time) , mathematics , computer science , artificial intelligence , kullback–leibler divergence , pattern recognition (psychology) , data mining , linguistics , philosophy , physics , quantum mechanics
The need of suitable divergence measures arise as they play an important role in discrimination of two probability distributions. The present communication is devoted to the introduction of one such divergence measure using Jensen inequality and Shannon entropy and its validation. Also, a new dissimilarity measure based on the proposed divergence measure is introduced. Besides establishing validation, some of its major properties are also studied. Further, a new multiple attribute decision making method based on a proposed dissimilarity measure is introduced and is thoroughly explained with the help of an illustrated example. The paper is summed up with an application of the proposed dissimilarity measure in pattern recognition.