Mercer kernel based hybrid C-means fuzzy clustering algorithm with dynamic weight
动态权值混合C-均值模糊核聚类算法*

PCM algorithm often tends to find the identical cluster. Proposed PFCM, which divides the data set into different clusters through producing memberships and possibilities simultaneously, along with the cluster centers. But when two highly imbalanced samples clusters are given, PFCM fails to give the desired results. In order to overcome the weakness, this paper firstly mapped the original data space to a high-dimensional feature space by Mercer kernel functions, and assigned an addtional weighting factor to each vector in the feature space. Then introduced a modified objective function for fuzzy clustering in the feature space. Theoretical analysis and experimented results testify that the new algorithm has more robust and higher clustering accuracy compared with those classic fuzzy clustering algorithm.