Viewpoint-Based Collaborative Feature-Weighted Multi-View Intuitionistic Fuzzy Clustering Using Neighborhood Information
Küçük Resim Yok
Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier B.V.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This 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.
Açıklama
Anahtar Kelimeler
Feature Weighting, Multi-view Clustering, Multi-View Fuzzy C-Means, Neighbourhood İnformation, Sample Weighting
Kaynak
Neurocomputing
WoS Q Değeri
Scopus Q Değeri
Q1
Cilt
617
Sayı
Künye
Golzari Oskouei, A., Samadi, N., Tanha, J., Bouyer, A., & Arasteh, B. (2025). Viewpoint‐Based Collaborative Feature‐Weighted Multi‐View Intuitionistic Fuzzy Clustering Using Neighborhood Information.
