License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ESA.2019.40
URN: urn:nbn:de:0030-drops-111613
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2019/11161/
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Ding, Hu ; Yu, Haikuo ; Wang, Zixiu

Greedy Strategy Works for k-Center Clustering with Outliers and Coreset Construction

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LIPIcs-ESA-2019-40.pdf (1 MB)

Abstract

We study the problem of k-center clustering with outliers in arbitrary metrics and Euclidean space. Though a number of methods have been developed in the past decades, it is still quite challenging to design quality guaranteed algorithm with low complexity for this problem. Our idea is inspired by the greedy method, Gonzalez's algorithm, for solving the problem of ordinary k-center clustering. Based on some novel observations, we show that this greedy strategy actually can handle k-center clustering with outliers efficiently, in terms of clustering quality and time complexity. We further show that the greedy approach yields small coreset for the problem in doubling metrics, so as to reduce the time complexity significantly. Our algorithms are easy to implement in practice. We test our method on both synthetic and real datasets. The experimental results suggest that our algorithms can achieve near optimal solutions and yield lower running times comparing with existing methods.

BibTeX - Entry

@InProceedings{ding_et_al:LIPIcs:2019:11161,
  author =	{Hu Ding and Haikuo Yu and Zixiu Wang},
  title =	{{Greedy Strategy Works for k-Center Clustering with Outliers and Coreset Construction}},
  booktitle =	{27th Annual European Symposium on Algorithms (ESA 2019)},
  pages =	{40:1--40:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-124-5},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{144},
  editor =	{Michael A. Bender and Ola Svensson and Grzegorz Herman},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2019/11161},
  URN =		{urn:nbn:de:0030-drops-111613},
  doi =		{10.4230/LIPIcs.ESA.2019.40},
  annote =	{Keywords: k-center clustering, outliers, coreset, doubling metrics, random sampling}
}

Keywords: k-center clustering, outliers, coreset, doubling metrics, random sampling
Collection: 27th Annual European Symposium on Algorithms (ESA 2019)
Issue Date: 2019
Date of publication: 06.09.2019

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