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《Modular Machine Tool & Automatic Manufacturing Technique》 2011-04
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The K-means Algorithm Improvement Base on the Number of Clustering k and Initial Centroid

QU Xin-huai,GAO Wang-li,DIMG Bi-rong,LI Zhen(School of Machinery and Automobile Engineering,Hefei University of Technology,Hefei 230009,China)  
In the original K-means algorithm,clustering can be done with the initial centroid generated randomly and the number of clustering k given in advance,and it reduces the effect of clustering greatly.In this paper,we improve the original K-means algorithm,and propose a new algorithm that the initial centroid is chose by the density and the number of clustering k is optimized by genetic algorithm.To a certain degree,this algorithm minimize the effects of initial centroid and the number of clustering k on the clustering accuracy and efficiency,and enhance the accuracy of clustering.We prove the validity of this improved algorithm by experiment at the end of this paper.
【CateGory Index】: TP311.13
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