关键词: 模糊聚类，拉子群优化，模糊C均值，可能性C均值

Abstract: An improved possibifistic Gmeans(PCM) algorithm based on particle swarm optimization (PSO) was pre- sented. This algorithm consists of two steps; first, using the improved PCM to calculate the degree of membership ma- trix and cluster centroid to encode particles, which can low the influence of initialized centroid and improve clustering precision. In the second, using PSO to optimize the encoded data points, which can overcome the coincident clusters and avoid easily falling into local optimum The experimental results on the synthetic data sets and UCI data sets show that the proposed algorithm has less computational complexity, higher clustering precision and greater searching capability.

Key words: Fuzzy clustering,Particle swarm optimization,Fuzzy C-means clustering,Possibilistic C-means clustering