Fuzzy C Means Clustering Algorithm Based on Particle Swarm Optimizing
XIN Hai-ming,TAO Zhi-sui(College of science,South China University of Technology,Guangzhou,Guangdong 510640,China)
The algorithm (YNPF) is proposed because particle swarm optimizing with cluster centers spends a redundant time. Firstly YNPF uses a fuzz validity criterion to ensure the best number of clustering, secondly uses a improved particle swarm (PSO) algorithm to optimize the centers of fuzz C means (WAFCM1]) clustering, lastly uses WAFCM again. The experiment result shows that the proposed algorithm can improve the classification correct rate, improve operation rapidity. The clustering effects are superior to original FCM algorithm or original PSO or hybrid clustering based on particle swarm optimization and FCM algorithm (PF) and NPF algorithm.
【CateGory Index】： TP18