关键词: 模糊C均值聚类,样本容量,均衡化,粒子群,全局优解

Abstract: Fuzzy C-means clustering(FCM) is a fast and effective clustering algorithm,but it doesn’t consider the difference of the samples size,while the capacities of each class are of large difference,and the decision of FCM will be benificial to the class with less samples．A new clustering algorithm was proposed in the paper and named as equalization fuzzy C-means clustering(EFCM).The minimum objective function of FCM was modified and the factor of samples size was added in EFCM objective function.The parameter optimal solutions of EFCM were calculated through PSO algorithm in which sample fuzzy memberships are seted as coding object．The properties of EFCM were obtained by theoretical analysis．The effectiveness of EFCM for balansed and unbalanced datasets was proved by simulation experiments．

Key words: Fuzzy C-means clustering,Samples size,Equalization,Particle swarm,Global optimal solution