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《Computer Engineering》 2008-15
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Clustering Algorithm Based on Immune Particle Swarm Optimization

ZHENG Xiao-ming,LV Shi-ying,WANG Xiao-dong(College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350002)  
K-means algorithm is simple and fast,however its result is affected by the initial clustering center and easily falls into the local optimum.This paper combines Particle Swarm Optimization(PSO) and adjusting mechanism and the immune memory function of immune system to improve K-means algorithm,and proposes a clustering algorithm based on Immune Particle Swarm Optimization algorithm(IM-PSO-KMEANS).The experiments show that the IM-PSO-KMEANS algorithm overcomes the problems of K-means algorithm,and the results of clustering are better than algorithm based on PSO.
【Fund】: 福建省自然科学基金资助项目(A0510008)
【CateGory Index】: TP301.6
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