Viewpoint-based collaborative feature-weighted multi-view intuitionistic fuzzy clustering using neighborhood information

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dc.authorscopusidAmin Golzari Oskouei / 57207307861
dc.authorscopusidAsgarali Bouyer / 35177297800
dc.authorscopusidBahman Arasteh / 39861139000
dc.authorwosidAmin Golzari Oskouei / S-4622-2019
dc.authorwosidAsgarali Bouyer / JOZ-6483-2023
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.accessioned2025-04-17T06:44:55Z
dc.date.available2025-04-17T06:44:55Z
dc.date.issued2024
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.
dc.identifier.citationOskouei, A. G., Samadi, N., Tanha, J., Bouyer, A., & Arasteh, B. (2025). Viewpoint‐Based Collaborative Feature‐Weighted Multi‐View Intuitionistic Fuzzy Clustering Using Neighborhood Information. Neurocomputing, 617, 128884.
dc.identifier.doi10.1016/j.neucom.2024.128884
dc.identifier.endpage26
dc.identifier.issn0925-2312
dc.identifier.issn1872-8286
dc.identifier.scopus2-s2.0-85210541366
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1016/j.neucom.2024.128884
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6100
dc.identifier.volume617
dc.identifier.wosWOS:001371449400001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorGolzari Oskouei, Amin
dc.institutionauthorBouyer, Asgarali
dc.institutionauthorArasteh, Bahman
dc.institutionauthoridAmin Golzari Oskouei / 0000-0003-2551-7105
dc.institutionauthoridAsgarali Bouyer / 0000-0002-4808-2856
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 Information
dc.subjectSample Weighting
dc.titleViewpoint-based collaborative feature-weighted multi-view intuitionistic fuzzy clustering using neighborhood information
dc.typeArticle

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