Viewpoint-Based Collaborative Feature-Weighted Multi-View Intuitionistic Fuzzy Clustering Using Neighborhood Information

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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.authorTanha, Jafar
dc.contributor.authorBouyer, Asgarali
dc.contributor.authorArasteh, Bahman
dc.date.accessioned2025-04-18T07:48:08Z
dc.date.available2025-04-18T07:48:08Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümü
dc.description.abstractThis paper presents an intuitionistic fuzzy c-means-based clustering algorithm for multi-view clustering, addressing key challenges such as noise sensitivity, outlier influence, and the distinct importance of views, features, and samples. Our proposed approach incorporates view weights, feature weights, sample weights, and neighborhood information into a novel objective function. Additionally, we introduce an effective initial cluster center selection strategy that enhances clustering robustness. The efficiency of the proposed method is evaluated using various clustering criteria (AR, NMI, RI, FMI, and JI). Moreover, the effect of each module of the algorithm on the general clustering performance is examined exclusively. Experimental results on various benchmark multi-view datasets demonstrate that our algorithm outperforms state-of-the-art methods in terms of clustering accuracy and stability. The source code of the proposed method is accessible at https://github.com/Amin-Golzari-Oskouei/VCoFWMVIFCM. © 2024 Elsevier B.V.
dc.identifier.citationGolzari Oskouei, A., Samadi, N., Tanha, J., Bouyer, A., & Arasteh, B. (2025). Viewpoint‐Based Collaborative Feature‐Weighted Multi‐View Intuitionistic Fuzzy Clustering Using Neighborhood Information.
dc.identifier.doi10.1016/j.neucom.2024.128884
dc.identifier.issn09252312
dc.identifier.scopus2-s2.0-85210541366
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.1016/j.neucom.2024.128884
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6494
dc.identifier.volume617
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 Weighting
dc.subjectMulti-view Clustering
dc.subjectMulti-View Fuzzy C-Means
dc.subjectNeighbourhood İnformation
dc.subjectSample Weighting
dc.titleViewpoint-Based Collaborative Feature-Weighted Multi-View Intuitionistic Fuzzy Clustering Using Neighborhood Information
dc.typeArticle

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