Approximate Range Queries for Clustering

Authors Eunjin Oh, Hee-Kap Ahn

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Eunjin Oh
Hee-Kap Ahn

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Eunjin Oh and Hee-Kap Ahn. Approximate Range Queries for Clustering. In 34th International Symposium on Computational Geometry (SoCG 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 99, pp. 62:1-62:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


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.
  • Approximate clustering
  • orthogonal range queries


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