Feature-weight and cluster-weight learning in fuzzy c-means method for semi-supervised clustering

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dc.authorscopusidAmin Golzari Oskouei / 57207307861
dc.authorwosidAmin Golzari Oskouei / S-4622-2019
dc.contributor.authorOskouei, Amin Golzari
dc.contributor.authorSamadi, Negin
dc.date.accessioned2025-04-18T10:18:32Z
dc.date.available2025-04-18T10:18:32Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümü
dc.description.abstractSemi -supervised clustering aims to guide the clustering by utilizing auxiliary information about the class labels. Among the semi -supervised clustering categories, the constraint -based approach uses the available pairwise constraints in some steps of the clustering procedure, usually by adding new terms to the objective function. Considering this category, Semi -supervised FCM (SSFCM) is a semi -supervised version of the fuzzy c -means algorithm, which takes advantage of fuzzy logic and auxiliary class distribution knowledge. Despite the performance enhancement caused by incorporating this extra knowledge in the clustering process, semi -supervised fuzzy approaches still suffer from some problems. All the data attributes in the feature space are assumed to have equal importance in the cluster formation, while some features may be more informative than others. Thus the feature importance issue is not addressed in the semi -supervised category. This paper proposes a novel SemiSupervised Fuzzy c -means approach, which is designed based on Feature -Weight, and Cluster -Weight learning, named SSFCM-FWCW. Inspired by the SSFCM, a fuzzy objective function is presented, which is composed of (1) a semi -supervised term representing the external class knowledge; (2) a feature weighting; and (3) a cluster weighting. Both feature weights and cluster weights are determined adaptively during the clustering. Considering these two techniques leads to insensitivity to the initial center selection, insensitivity to noise, and consequently helps to form an optimal clustering structure. Experimental comparisons are carried out on several benchmark datasets to evaluate the proposed approach 's performance, and promising results are achieved.
dc.identifier.citationGolzari Oskouei, A., Samadi, N., & Tanha, J. (2024). Feature-weight and cluster-weight learning in fuzzy c-means method for semi-supervised clustering▪.
dc.identifier.doi10.1016/j.asoc.2024.111712
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-85193059469
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.1016/j.asoc.2024.111712
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7020
dc.identifier.volume161
dc.identifier.wosWOS:001242318700001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorOskouei, Amin Golzari
dc.institutionauthoridAmin Golzari Oskouei / 0000-0003-2551-7105
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofApplied soft computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectSemi-Supervised Clustering
dc.subjectFuzzy c-Means
dc.subjectFeature Weighting
dc.titleFeature-weight and cluster-weight learning in fuzzy c-means method for semi-supervised clustering
dc.typeArticle

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