Feature-weighted fuzzy clustering methods: An experimental review

• Published in: Neurocomputing (Amsterdam) Vol. 619; p. 129176
• Main Authors: Golzari Oskouei, Amin, Samadi, Negin, Khezri, Shirin, Najafi Moghaddam, Arezou, Babaei, Hamidreza, Hamini, Kiavash, Fath Nojavan, Saghar, Bouyer, Asgarali, Arasteh, Bahman
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 28.02.2025
• Subjects: Feature Importance; Feature Selection; Feature Weighting; Fuzzy C-Means; Review; Soft Clustering; Feature Selection; Feature Weighting; Fuzzy C-Means; Soft Clustering; Review; Feature Importance
• ISSN: 0925-2312
• DOI: 10.1016/j.neucom.2024.129176

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. •This paper reviews fuzzy clustering algorithms using feature weighting techniques in depth and detail.•State-of-the-art methods are implemented in Python and compared based on clustering performance in experiments.•An experimental analysis evaluates 26 clustering algorithms on synthetic and 14 UCI benchmark datasets.•Experimental results' significance is tested using Friedman and Holm’s post-hoc statistical methods.