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Communication Dans Un Congrès Année : 2019

Near-linear time approximations schemes for clustering in doubling metrics

Résumé

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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Dates et versions

hal-02360768 , version 1 (13-11-2019)

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Vincent Cohen-Addad, Andreas Emil 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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