Adaptive Sampling for k-Means Clustering

被引：0
|
作者：
Aggarwal, Ankit [1 ]
Deshpande, Amit [2 ]
Kannan, Ravi [2 ]
机构：
[1] Indian Inst Technol Delhi, Delhi, India
[2] Microsoft Res India, Bangalore, Karnataka, India
来源：
APPROXIMATION, RANDOMIZATION, AND COMBINATORIAL OPTIMIZATION: ALGORITHMS AND TECHNIQUES | 2009年 / 5687卷
关键词：
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
We show that adaptively Sampled O(k) centers, give a constant factor bi-criteria approximation for the k-means problem, with a constant probability. Moreover, these O(k) centers contain a subset of k centers which give a constant factor approximation, and can be found using LP-based techniques of Jain mid Vazirani [JV01] and Charikar et al. [CGTS02]. Both these algorithms mill in effectively O(nkd) time mid extend the O(log k)-approximation achieved by the k-means++ algorithm of Arthur and Vassilvitskii [AV07].
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页码：15 / +
页数：3
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