{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:54:37Z","timestamp":1777704877432,"version":"3.51.4"},"reference-count":31,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,1,4]]},"abstract":"<jats:p>Fuzzy C-means clustering algorithm (FCM) is an effective approach for clustering. However, in most existing FCM type frameworks, only in-cluster compactness is taken into account, whereas the between-cluster separability is overlooked. In this paper, to enhance the clustering, by incorporating the feature weighting and data weighting method, we put forward a new weighted fuzzy C-means clustering approach considering between-cluster separability, in which for achieving good compactness and separability, making the in-cluster distances as small as possible and making the between-cluster distances as large as possible, the in-cluster distances and between-cluster distances are taken into account; To achieve the optimal clustering result, the iterative formulas of the feature weights, membership degrees, data weights and cluster centers are obtained by maximizing the in-cluster compactness and the between-cluster separability. Experiments on real-world datasets were carried out, the results showed that the new approach could obtain promising performance.<\/jats:p>","DOI":"10.3233\/jifs-201178","type":"journal-article","created":{"date-parts":[[2020,10,30]],"date-time":"2020-10-30T12:34:07Z","timestamp":1604061247000},"page":"1017-1024","source":"Crossref","is-referenced-by-count":2,"title":["A new weighted fuzzy C-means clustering approach considering between-cluster separability"],"prefix":"10.1177","volume":"40","author":[{"given":"Ziheng","family":"Wu","sequence":"first","affiliation":[{"name":"School of Electrical and Information Engineering, AnHui University of Technology, Maanshan, China"},{"name":"Anhui Province Key Laboratory of Special and Heavy Load Robot, Maanshan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, AnHui University of Technology, Maanshan, 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