Improved k-means initial clustering center selection algorithm
HAN Ling-bo1,WANG Qiang2,JIANG Zheng-feng2,HAO Zhi-qiang21.Department of Theory and Information,Zhanjiang Party Institute,Zhanjiang,Guangdong 524032,China 2.College of Computer Science and Information Technology,Guangxi Normal University,Guilin,Guangxi 541004,China
The traditional k-means has sensitivity to the initial clustering center.Considering this defection,a new improved algorithm is proposed.In the new algorithm,the density parameter of every data object is computed,and then k data objects with high density parameter are chosen as the initial clustering centers.Given the cluster number,and UCI database is used as testing datasets.The clustering results demonstrate that the improved algorithm can enhance the clustering stability and accuracy of ordinary k-means algorithm relatively.
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