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Adaptive Sampling for k-Means Clustering

  • Ankit Aggarwal
  • Amit Deshpande
  • Ravi Kannan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5687)

Abstract

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 and Vazirani [JV01] and Charikar et al. [CGTS02]. Both these algorithms run in effectively O(nkd) time and extend the O(logk)-approximation achieved by the k-means++ algorithm of Arthur and Vassilvitskii [AV07].

Keywords

Adaptive Sampling Constant Probability Constant Factor Approximation Constant Factor Approximation Algorithm Parallel Axis Theorem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ankit Aggarwal
    • 1
  • Amit Deshpande
    • 2
  • Ravi Kannan
    • 2
  1. 1.IIT DelhiIndia
  2. 2.Microsoft ResearchIndia

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