Research on PSO-based intuitionistic fuzzy kernel clustering algorithm - UXiao-dong Y; EIYing-jie L; UEShao-hua Y; ANGRui W (Tongxin xuebao)
The intuitionistic fuzzy kernel c-means clustering algorithm has several problems such as sensitivity to the ini-tial value,low convergence speed,etc.To overcome these shortages,the particle swarm optimization (PSO) algorithm with powerful ability of global search and quick convergence rate is applied to intuitionistic fuzzy clustering.Firstly,PSO is used to optimize the initial clustering centers.Then,the approach of intuitionistic fuzzy kernel clustering based on PSO,namely PS-IFKCM,is proposed.This algorithm can enhance both the clustering ability and the convergence speed.Fi-nally,experiments based on four measured datasets are carried out to illustrate the performance of the proposed method.Compared with results from FCM and IFKCM,PS-IFKCM is of great efficiency for classification.