Affiliations: [a] School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China and also with School of Data Science and Computer Science, Guizhou Minzu University, Guiyang, China
| [b] Key Laboratory of Pattern and Intelligent System of Guizhou Province, Guizhou Minzu University, Guiyang, China
| [c] Engineering Training Center, Guizhou Minzu University, Guiyang, China
| [d] School of Artificial Intelligence and Computer Science, and the Key Lab. of Media Design and Software Technologies of Jiangsu, Jiangnan University, Wuxi, China
| [e] School of Economics, Nanjing University of Finance and Economics, Nanjing, China
Correspondence:
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Corresponding author. Shitong Wang, School of Artificial Intelligence and Computer Science and the Key Lab. of Media Design and Software Technologies of Jiangsu Province, Jiangnan University, Wuxi 214122, China. E-mail: wxwangst@aliyun.com.
Abstract: In feature weighted fuzzy c-means algorithms, there exist two challenges when the feature weighting techniques are used to improve their performances. On one hand, if the values of feature weights are learnt in advance, and then fixed in the process of clustering, the learnt weights might be lack of flexibility and might not fully reflect their relevance. On the other hand, if the feature weights are adaptively adjusted during the clustering process, the algorithms maybe suffer from bad initialization and lead to incorrect feature weight assignment, thus the performance of the algorithms may degrade the in some conditions. In order to ease these problems, a novel weighted fuzzy c-means based on feature weight learning (FWL-FWCM) is proposed. It is a hybrid of fuzzy weighted c-means (FWCM) algorithm with Improved FWCM (IFWCM) algorithm. FWL-FWCM algorithm first learns feature weights as priori knowledge from the data in advance by minimizing the feature evaluation function using the gradient descent technique, then iteratively optimizes the clustering objective function which integrates the within weighted cluster dispersion with a term of the discrepancy between the weights and the priori knowledge. Experiments conducted on an artificial dataset and real datasets demonstrate the proposed approach outperforms the-state-of-the-art feature weight clustering methods. The convergence property of FWL-FWCM is also presented.
