Unsupervised feature selection via fuzzy c-means clustering and binary atom search algorithm | ||||
| Computational Journal of Mathematical and Statistical Sciences | ||||
| Articles in Press, Accepted Manuscript, Available Online from 21 June 2025 PDF (428.8 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/cjmss.2025.363038.1130 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Hanadi Saleem; Talal Hussein; Fatima mahmood Hasan  ; Omar Qasim
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| Department of Mathematics, College of Computer Science and Mathematics, University of Mosul, Mosul 41002, Iraq | ||||
| Abstract | ||||
| This paper presents a proposed algorithm that combines the Binary Atomic Search (BAS) algorithm and the Fuzzy C-means Method (FCM) technique, called BAS-FCM. The proposed algorithm, BAS-FCM, is based on two-stage data processing. The first stage involves selecting important features from the datasets and discarding unimportant ones using the BAS algorithm, which relies on a proposed fitness function to test feature sets and select the best ones for classification and clustering. The second stage involves classifying an unsupervised datasets using FCM. This innovative combination leverages both features to improve accuracy, efficiency, and usefulness on high-dimensional datasets. Experiments on five different datasets demonstrate that the proposed method consistently achieves higher scores for silhouettes with fewer selected features compared to the default clustering technique. These results demonstrate the effectiveness and robustness of the proposed BAS-FCM method compared to the classical method, making it a promising approach for improving the performance of clustering-based feature selection. | ||||
| Keywords | ||||
| Fuzzy C-means; Binary Atom Search; Feature Selection; Clustering | ||||
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