An adaptive approach of Feature Selection applied to Semi-Supervised Fuzzy Clustering
Wei Cai, Shengbing Xu, Jiongzhi Liu, Qingping Du, Hefeng Chen, Yinyun Lin · Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering · 2020
Label information in the corresponding semi-supervised fuzzy clustering cannot be used efficiently due to feature redundancy. To address the problem, we propose an adaptive approach of feature selection applied to semi-supervised fuzzy clustering. There are three phases in our approach: 1) feature-score by fisher-score; 2) Min Mean Square Error of Feature Select criterion; 3) the number of features is selected by Min Mean Square Error of Feature Select criterion. We apply our approach to three semi-supervised fuzzy clustering methods. Experiments show that the adaptive approach of feature selection applied to semi-supervised fuzzy clustering can improve the clustering performance.