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dc.contributor.authorJaehoon Chaen_US
dc.contributor.authorSanghyuk Leeen_US
dc.contributor.authorKyeong Soo Kimen_US
dc.contributor.authorWitold Pedryczen_US
dc.description.abstract© 2017 IEEE. Similarity measure for fuzzy sets is designed with the help of a conventional fuzzy measure and integral. Similarity measure based on fuzzy integral not only evaluates similarity but also captures the characteristics occurring between various data sets. Compared to a conventional approach based on a distance measure, the proposed similarity measure based on fuzzy integral delivers additional information that convergence in similarity value provides data comparison structure between data sets. The properties of the proposed similarity measure are analyzed and demonstrated with illustrative examples. The degree of each data set and its distribution plays a crucial role in discriminating data characteristics. The designed similarity measure shows its convergence. Comparison with random data is carried out, and its similarity value and convergence properties are analyzed with the use of the similarity measure.en_US
dc.subjectComputer Scienceen_US
dc.titleOn the design of similarity measures based on fuzzy integralen_US
dc.typeConference Proceedingen_US
article.title.sourcetitleIFSA-SCIS 2017 - Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systemsen_US'an Jiaotong-Liverpool Universityen_US Mai Universityen_US of Albertaen_US
Appears in Collections:CMUL: Journal Articles

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