Journal
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
Volume 34, Issue 8, Pages 1795-1834Publisher
WILEY
DOI: 10.1002/int.22115
Keywords
clustering analysis; information measures; medical diagnosis; q-rung orthopair fuzzy sets; similarity measures
Categories
Funding
- National Natural Science Foundation of China [61462019]
- Ministry of Education [18YJCZH054]
- Natural Science Foundation of Guangdong Province [2018A0303130274, 2018A030307033]
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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.
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