k-均值聚类问题的改进PTAS逼近算法

2012 2nd International Conference on Uncertainty Reasoning and Knowledge Engineering Pub Date : 2012-10-04 DOI:10.1109/URKE.2012.6319592

Wang Shou-qiang

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引用次数: 2

摘要

本文提出了Ostrovsky提出的一种改进的(1+ε)-随机逼近算法。改进算法的运行时间为O(2(O(kα2/ε))nd)，其中d、n分别表示输入点的维数和个数，α((1/2ε)))k(1-O(√α))。与原算法相比，改进后的算法运行效率更高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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An improved PTAS approximation algorithm for k-means clustering problem

This paper presented an improved (1+ε)-randomized approximation algorithm proposed by Ostrovsky. The running time of the improved algorithm is O(2(O(kα²/ε))nd), 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 (1/2(1-e^(1/2ε)))k(1-O(√α)). Compared to the original algorithm, the improved algorithm runs more efficiency.

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2012 2nd International Conference on Uncertainty Reasoning and Knowledge Engineering

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