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DOI: 10.4230/LIPIcs.APPROX/RANDOM.2021.16

Min-Sum Clustering (With Outliers)

Authors: Sandip Banerjee, Rafail Ostrovsky, and Yuval Rabani

Published in: LIPIcs, Volume 207, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2021)

Abstract

We give a constant factor polynomial time pseudo-approximation algorithm for min-sum clustering with or without outliers. The algorithm is allowed to exclude an arbitrarily small constant fraction of the points. For instance, we show how to compute a solution that clusters 98% of the input data points and pays no more than a constant factor times the optimal solution that clusters 99% of the input data points. More generally, we give the following bicriteria approximation: For any ε > 0, for any instance with n input points and for any positive integer n' ≤ n, we compute in polynomial time a clustering of at least (1-ε) n' points of cost at most a constant factor greater than the optimal cost of clustering n' points. The approximation guarantee grows with 1/(ε). Our results apply to instances of points in real space endowed with squared Euclidean distance, as well as to points in a metric space, where the number of clusters, and also the dimension if relevant, is arbitrary (part of the input, not an absolute constant).

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Sandip Banerjee, Rafail Ostrovsky, and Yuval Rabani. Min-Sum Clustering (With Outliers). In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 207, pp. 16:1-16:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{banerjee_et_al:LIPIcs.APPROX/RANDOM.2021.16,
  author =	{Banerjee, Sandip and Ostrovsky, Rafail and Rabani, Yuval},
  title =	{{Min-Sum Clustering (With Outliers)}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2021)},
  pages =	{16:1--16:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-207-5},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{207},
  editor =	{Wootters, Mary and Sanit\`{a}, Laura},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2021.16},
  URN =		{urn:nbn:de:0030-drops-147093},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2021.16},
  annote =	{Keywords: Clustering, approximation algorithms, primal-dual}
}

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1 Search Results for "Banerjee, Sandip"

Min-Sum Clustering (With Outliers)

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