关键词: 模糊C-均值聚类, 可能性聚类, 核聚类

Abstract: Most of the traditional fast clustering algorithms are based on fuzzy C means algorithm（Fuzzy C-means，FCM），while FCM is sensitive to the initial cluster centers and noises and is prone to converge to local minimum values，leading to low clustering accuracy.The possibilistic C-means clustering algorithm solves the noise-sensitivity for FCM but tends to find identical clusters.The clustering algorithm which combines FCM and the possibilistic C-means clustering algorithm，solves the problem of coincidence.In order to further improve the speed of convergence and the robustness of the algorithm，this paper proposes a kernel-based fast improved possibilistic C-means clustering algorithm.The algorithm introduces the idea of kernel clustering，and at the same time，optimizes the parameter η in the objective function by the help of the sample variance.The results of the experiments on standard data sets and synthetic data sets show that the kernel-based fast improved possibilistic C-means clustering algorithm can improve the accuracy of clustering and the speed of convergence.

Key words: fuzzy C-means clustering, possibilistic clustering, kernel clustering