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DOI: 10.4230/LIPIcs.ESA.2017.48
URN: urn:nbn:de:0030-drops-78749
URL: http://drops.dagstuhl.de/opus/volltexte/2017/7874/
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Henzinger, Monika ; Leniowski, Dariusz ; Mathieu, Claire

Dynamic Clustering to Minimize the Sum of Radii

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LIPIcs-ESA-2017-48.pdf (0.4 MB)


Abstract

In this paper, we study the problem of opening centers to cluster a set of clients in a metric space so as to minimize the sum of the costs of the centers and of the cluster radii, in a dynamic environment where clients arrive and depart, and the solution must be updated efficiently while remaining competitive with respect to the current optimal solution. We call this dynamic sum-of-radii clustering problem. We present a data structure that maintains a solution whose cost is within a constant factor of the cost of an optimal solution in metric spaces with bounded doubling dimension and whose worst-case update time is logarithmic in the parameters of the problem.

BibTeX - Entry

@InProceedings{henzinger_et_al:LIPIcs:2017:7874,
  author =	{Monika Henzinger and Dariusz Leniowski and Claire Mathieu},
  title =	{{Dynamic Clustering to Minimize the Sum of Radii}},
  booktitle =	{25th Annual European Symposium on Algorithms (ESA 2017)},
  pages =	{48:1--48:10},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-049-1},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{87},
  editor =	{Kirk Pruhs and Christian Sohler},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2017/7874},
  URN =		{urn:nbn:de:0030-drops-78749},
  doi =		{10.4230/LIPIcs.ESA.2017.48},
  annote =	{Keywords: dynamic algorithm, clustering, approximation, doubling dimension}
}

Keywords: dynamic algorithm, clustering, approximation, doubling dimension
Seminar: 25th Annual European Symposium on Algorithms (ESA 2017)
Issue Date: 2017
Date of publication: 31.08.2017


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