Feature-weighted fuzzy clustering methods: An experimental review

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dc.authorscopusidBahman Arasteh / 39861139000
dc.authorwosidBahman Arasteh / AAN-9555-2021
dc.contributor.authorGolzari Oskouei, Amin
dc.contributor.authorSamadi, Negin
dc.contributor.authorKhezri, Shirin
dc.contributor.authorNajafi Moghaddam, Arezou
dc.contributor.authorBabaei, Hamidreza
dc.contributor.authorHamini, Kiavash
dc.contributor.authorFath Nojavan, Saghar
dc.contributor.authorBouyer, Asgarali
dc.contributor.authorArasteh, Bahman
dc.date.accessioned2025-04-18T10:35:46Z
dc.date.available2025-04-18T10:35:46Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümü
dc.description.abstractSoft clustering, a widely utilized method in data analysis, offers a versatile and flexible strategy for grouping data points. Most soft clustering algorithms assume that all the features present in the feature space of a dataset are of equal importance and neglect their degree of informativeness or irrelevance. Distinguishing between the relative importance of features in providing an optimal clustering structure has become a very challenging task. Many feature weighting methods have been proposed to deal with this problem in the field of soft clustering, which can broadly categorized into six major types: feature reduction-based, entropy-based, variance-based, membership-based, optimization-based, and meta-heuristic-based. This paper comprehensively reviews the most significant fuzzy clustering algorithms that employ feature weighting techniques. A taxonomy of the feature weighting-based fuzzy clustering algorithms is presented. Furthermore, all state-of-the-art approaches are implemented in Python and compared in terms of clustering performance by conducting various experimental evaluation schemes. In this comprehensive experimental analysis, 26 state-of-the-art clustering algorithms are evaluated on two synthetic and 18 benchmark UCI datasets based on Accuracy (ACC), Normalized Mutual Information (NMI), Precision (PR), Recall (RE), F1, Silhouette (SI) and Davies-Bouldin (DB) evaluation criteria. Moreover, the significance of the experimental comparisons is examined using Friedman and Holm's post-hoc statistical tests. The experimental analysis demonstrates the superior performance of variance-based feature weighting algorithms in most datasets. All the tested algorithms are implemented in Python, and the related source codes are shared publicly at https://github.com/Amin-Golzari-Oskouei/FWSCA. © 2024 Elsevier B.V.
dc.identifier.citationOskouei, A. G., Samadi, N., Khezri, S., Moghaddam, A. N., Babaei, H., Hamini, K., ... & Arasteh, B. (2024). Feature-Weighted Fuzzy Clustering Methods: An Experimental Review. Neurocomputing, 129176.
dc.identifier.doi10.1016/j.neucom.2024.129176
dc.identifier.issn09252312
dc.identifier.scopus2-s2.0-85212341661
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.1016/j.neucom.2024.129176
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7134
dc.identifier.volume619
dc.indekslendigikaynakScopus
dc.institutionauthorArasteh, Bahman
dc.institutionauthoridBahman Arasteh / 0000-0001-5202-6315
dc.language.isoen
dc.publisherElsevier B.V.
dc.relation.ispartofNeurocomputing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectFeature Importance
dc.subjectFeature Selection
dc.subjectFeature Weighting
dc.subjectFuzzy C-Means
dc.titleFeature-weighted fuzzy clustering methods: An experimental review
dc.typeOther

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