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An improved PTAS approximation algorithm for k-means clustering problem

机译:K-Means聚类问题的改进PTA近似算法

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This paper presented an improved (1+ε)-randomized approximation algorithm proposed by Ostrovsky. The running time of the improved algorithm is equation, where d,n denote the dimension and the number of the input points respectively, and α(<1) represents the separated coefficient. The successful probability is equation. Compared to the original algorithm, the improved algorithm runs more efficiency.
机译:本文提出了奥斯特罗夫斯基提出的改进的(1±) - andomization近似算法。改进算法的运行时间是等式,其中d,n分别表示输入点的尺寸和数量,α(<1)表示分离的系数。成功的概率是等式。与原始算法相比,改进的算法更高效率。

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