A K-means Algorithm Based on Optimized Initial Center Points
WANG Zhong, LIU Gui-Quan, CHEN En-Hong
1.Department of Computer Science and Technology, University of Science and Technology of China, Hefei 230027 2.Key Laboratory of Software in Computing and Communications of Anhui Province, Hefei 230027
Abstract：Aiming at the problems of K-means algorithm, a method is proposed to optimize the initial center points through computing the density of objects. Thus, the initial center of the samples can be built in a heuristic way. Then, a new evaluation function is proposed, namely equalization function, and consequently the cluster number is generated automatically. Compared with the traditional algorithms, the proposed algorithm can get initial centers with higher quality and steadier cluster results. Experimental results show the effectiveness and feasibility of the proposed algorithm.
汪中，刘贵全，陈恩红. 一种优化初始中心点的K-means算法*[J]. 模式识别与人工智能, 2009, 22(2): 299-304.
WANG Zhong, LIU Gui-Quan, CHEN En-Hong. A K-means Algorithm Based on Optimized Initial Center Points. , 2009, 22(2): 299-304.
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2009, Vol. 22 
