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K-Means Clustering Analysis Based on Genetic Algorithm

LAI Yu-xia1,LIU Jian-ping1,YANG Guo-xing2(1.College of Information and Electronics,Zhejiang Science and Technology University,Hangzhou 310018;2.Zhejiang PanChina Accounting Firm,Hangzhou 310012)  
Traditional K-Means algorithm is sensitive to the initial centers and easy to get stuck at locally optimal value.To solve such problems,this paper presents an improved K-Means algorithm based on genetic algorithm.It combines the locally searching capability of the K-Means with the global optimization capability of genetic algorithm,and introduces the K-Means operation into the genetic algorithm of adaptive crossover probability and adaptive mutation probability,which overcomes the sensitivity to the initial start centers and locality of K-Means.Experimental results demonstrate that the algorithm has greater global searching capability and can get better clustering.
【CateGory Index】: TP301.6
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