School of Control and Computer Engineering, North China Electric Power University, Baoding, Hebei, China.

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Abstract—The initial clustering centers of traditional bisecting K-means algorithm are randomly selected and the k value of traditional bisecting K-means algorithm could not determine beforehand. This paper proposes a improve bisecting K-means algorithm based on automatically determining K value and the optimization of the cluster center. Firstly, the initial cluster centers are selected by using the point density and the distance function; Secondly, automatically determining K value is proposed by using Intra cluster similarity and inter cluster difference. the experiment results on UCI database show that the algorithm can effectively avoid the influence of noise points and outliers, and improve the accuracy and stability of clustering results.

Index Terms—Bisecting k-means, K, cluster center, accuracy rate.

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Cite: Jian Di, Xinyue Gou, "Bisecting K-means Algorithm Based on K-valued Selfdetermining and Clustering Center Optimization," Journal of Computers vol. 13, no. 6, pp. 588-595, 2018.