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URN: urn:nbn:de:0030-drops-24862
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Online Correlation Clustering

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Abstract

We study the online clustering problem where data items arrive in an online fashion. The algorithm maintains a clustering of data items into similarity classes. Upon arrival of v, the relation between v and previously arrived items is revealed, so that for each u we are told whether v is similar to u. The algorithm can create a new luster for v and merge existing clusters. When the objective is to minimize disagreements between the clustering and the input, we prove that a natural greedy algorithm is O(n)-competitive, and this is optimal. When the objective is to maximize agreements between the clustering and the input, we prove that the greedy algorithm is .5-competitive; that no online algorithm can be better than .834-competitive; we prove that it is possible to get better than 1/2, by exhibiting a randomized algorithm with competitive ratio .5+c for a small positive fixed constant c.

BibTeX - Entry

@InProceedings{mathieu_et_al:LIPIcs:2010:2486,
  author =	{Claire Mathieu and Ocan Sankur and Warren Schudy},
  title =	{{Online Correlation Clustering}},
  booktitle =	{27th International Symposium on Theoretical Aspects of Computer Science},
  pages =	{573--584},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-16-3},
  ISSN =	{1868-8969},
  year =	{2010},
  volume =	{5},
  editor =	{Jean-Yves Marion and Thomas Schwentick},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2010/2486},
  URN =		{urn:nbn:de:0030-drops-24862},
  doi =		{http://dx.doi.org/10.4230/LIPIcs.STACS.2010.2486},
  annote =	{Keywords: Correlation clustering, online algorithms}
}

Keywords: Correlation clustering, online algorithms
Seminar: 27th International Symposium on Theoretical Aspects of Computer Science
Issue date: 2010
Date of publication: 2010


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