Affiliations: [a] School of Electrical and Information Engineering, AnHui University of Technology, Maanshan, China
| [b] Anhui Province Key Laboratory of Special and Heavy Load Robot, Maanshan, China
Correspondence:
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Corresponding author. Ziheng Wu, School of Electrical and Information Engineering, AnHui University of Technology, Maanshan, China. E-mail: wziheng@ahut.edu.cn.
Abstract: Fuzzy C-means clustering algorithm (FCM) is an effective approach for clustering. However, in most existing FCM type frameworks, only in-cluster compactness is taken into account, whereas the between-cluster separability is overlooked. In this paper, to enhance the clustering, by incorporating the feature weighting and data weighting method, we put forward a new weighted fuzzy C-means clustering approach considering between-cluster separability, in which for achieving good compactness and separability, making the in-cluster distances as small as possible and making the between-cluster distances as large as possible, the in-cluster distances and between-cluster distances are taken into account; To achieve the optimal clustering result, the iterative formulas of the feature weights, membership degrees, data weights and cluster centers are obtained by maximizing the in-cluster compactness and the between-cluster separability. Experiments on real-world datasets were carried out, the results showed that the new approach could obtain promising performance.
Keywords: Fuzzy C-means, data weighting, feature weighting, between-cluster
separability
