Near-linear time approximations schemes for clustering in doubling metrics

Abstract : We consider the classic Facility Location, k-Median, and k-Means problems in metric spaces of constant doubling dimension. We give the first nearly linear-time approximation schemes for each problem, making a significant improvement over the state-of-the-art algorithms. Moreover, we show how to extend the techniques used to get the first efficient approximation schemes for the problems of prize-collecting k-Medians and k-Means, and efficient bicriteria approximation schemes for k-Medians with outliers, k-Means with outliers and k-Center.
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Conference papers
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Contributor : Vincent Cohen-Addad <>
Submitted on : Wednesday, November 13, 2019 - 9:13:36 AM
Last modification on : Wednesday, December 4, 2019 - 8:14:19 PM
Long-term archiving on: Friday, February 14, 2020 - 2:41:29 PM


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  • HAL Id : hal-02360768, version 1
  • ARXIV : 1812.08664


Vincent Cohen-Addad, Andreas Feldmann, David Saulpic. Near-linear time approximations schemes for clustering in doubling metrics. FOCS'19, Nov 2019, Baltimore, United States. ⟨hal-02360768⟩



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