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.SoCG.2018.62
URN: urn:nbn:de:0030-drops-87755
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Oh, Eunjin ; Ahn, Hee-Kap

Approximate Range Queries for Clustering

LIPIcs-SoCG-2018-62.pdf (0.5 MB)


We study the approximate range searching for three variants of the clustering problem with a set P of n points in d-dimensional Euclidean space and axis-parallel rectangular range queries: the k-median, k-means, and k-center range-clustering query problems. We present data structures and query algorithms that compute (1+epsilon)-approximations to the optimal clusterings of P cap Q efficiently for a query consisting of an orthogonal range Q, an integer k, and a value epsilon>0.

BibTeX - Entry

  author =	{Eunjin Oh and Hee-Kap Ahn},
  title =	{{Approximate Range Queries for Clustering}},
  booktitle =	{34th International Symposium on Computational Geometry (SoCG 2018)},
  pages =	{62:1--62:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-066-8},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{99},
  editor =	{Bettina Speckmann and Csaba D. T{\'o}th},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-87755},
  doi =		{10.4230/LIPIcs.SoCG.2018.62},
  annote =	{Keywords: Approximate clustering, orthogonal range queries}

Keywords: Approximate clustering, orthogonal range queries
Collection: 34th International Symposium on Computational Geometry (SoCG 2018)
Issue Date: 2018
Date of publication: 08.06.2018

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