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    Feature-weight and cluster-weight learning in fuzzy c-means method for semi-supervised clustering
    (Elsevier, 2024) Oskouei, Amin Golzari; Samadi, Negin
    Semi -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.
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    Feature-weighted fuzzy clustering methods: An experimental review
    (Elsevier B.V., 2025) Golzari Oskouei, Amin; Samadi, Negin; Khezri, Shirin; Najafi Moghaddam, Arezou; Babaei, Hamidreza; Hamini, Kiavash; Fath Nojavan, Saghar; Bouyer, Asgarali; Arasteh, Bahman
    Soft 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.
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    Viewpoint-Based Collaborative Feature-Weighted Multi-View Intuitionistic Fuzzy Clustering Using Neighborhood Information
    (Elsevier B.V., 2025) Golzari Oskouei, Amin; Samadi, Negin; Tanha, Jafar; Bouyer, Asgarali; Arasteh, Bahman
    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.
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    Viewpoint-based collaborative feature-weighted multi-view intuitionistic fuzzy clustering using neighborhood information
    (Elsevier b.v., 2024) Golzari Oskouei, Amin; Samadi, Negin; Tanha, Jafar; Bouyer, Asgarali; Arasteh, Bahman
    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.