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Information measures for q ‐rung orthopair fuzzy sets
Author(s) -
Peng Xindong,
Liu Lin
Publication year - 2019
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.22115
Subject(s) - measure (data warehouse) , similarity measure , entropy (arrow of time) , mathematics , fuzzy set , fuzzy logic , cluster analysis , similarity (geometry) , data mining , set (abstract data type) , computer science , fuzzy measure theory , artificial intelligence , pattern recognition (psychology) , fuzzy number , image (mathematics) , physics , quantum mechanics , programming language
The q ‐rung orthopair fuzzy set ( q ‐ROFS), originally developed by Yager, is more capable than that of Pythagorean fuzzy set to deal uncertainty in real life. The main goal of this paper is to investigate the relationship between the distance measure, the similarity measure, the entropy, and the inclusion measure for q ‐ROFSs. The primary purpose of the study is to develop the systematic transformation of information measures (distance measure, similarity measure, entropy, and inclusion measure) for q ‐ROFSs. For obtaining this goal, some new formulae for information measures of q ‐ROFSs are presented. To show the validity of the explored similarity measure, we apply it to pattern recognition, clustering analysis, and medical diagnosis. Some illustrative examples are given to support the findings, and also demonstrate their practicality and availability of similarity measure between q ‐ROFSs.