Rough fuzzy k-means clustering based on parametric decision-theoretic shadowed set with three-way approximation

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / FPE-7309-2022
dc.contributor.authorZhang, Yudi
dc.contributor.authorZhang, Tengfei
dc.contributor.authorPeng, Chen
dc.contributor.authorMa, Fumin
dc.date.accessioned2025-04-18T10:00:30Z
dc.date.available2025-04-18T10:00:30Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractRough fuzzy K-means (RFKM) decomposes data into clusters using partial memberships by underlying structure of incomplete information, which emphasizes the uncertainty of objects located in cluster boundary. In this scheme, the settings of cluster boundary merely depend on subjective judgment of perceptual experience. When confronted with the data exhibiting heavily overlap and imbalance, the boundary regions obtained by existing empirical schemes vary greatly accompanied by skewing of cluster center, which exerts considerable influence on the accuracy and stability of RFKM. This paper seeks to analyze and address this deficiency and then proposes an improved rough fuzzy K-means clustering based on parametric decision-theoretic shadowed set (RFKM-DTSS). Three-way approximation is implemented by incorporating a novel fuzzy entropy into the decision-theoretic shadowed set, which rationalizes cluster boundary through minimizing fuzzy entropy loss. Under the secondary adjustment method and improved update strategy of cluster center, the proposed RFKM-DTSS is thus featured by a powerful processing ability on class overlap and imbalance commonly seen in scenarios, such as fault detection and medical diagnosis with unclear decision boundaries. The effectiveness and robustness of the RFKM-DTSS are verified by the results of comparative experiments, demonstrating the superiority of the proposed algorithm.
dc.description.sponsorshipNational Natural Science Foundation of China (NSFC)
dc.identifier.citationZhang, Y., Zhang, T., Peng, C., Ma, F., & Pedrycz, W. (2024). Rough Fuzzy K-Means Clustering Based on Parametric Decision-Theoretic Shadowed Set with Three-Way Approximation. International Journal of Fuzzy Systems, 1-18.
dc.identifier.doi10.1007/s40815-024-01700-8
dc.identifier.endpage1715
dc.identifier.issn1562-2479
dc.identifier.issn2199-3211
dc.identifier.issue5
dc.identifier.scopusqualityQ1
dc.identifier.startpage1698
dc.identifier.urihttp://dx.doi.org/10.1007/s40815-024-01700-8
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6914
dc.identifier.volume26
dc.identifier.wosWOS:001237821900003
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofInternational journal of fuzzy systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectRough Fuzzy K-Means
dc.subjectThree-Way Approximation
dc.subjectDecision-Theoretic Shadowed Sets
dc.subjectOverlapping Custers
dc.titleRough fuzzy k-means clustering based on parametric decision-theoretic shadowed set with three-way approximation
dc.typeArticle

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