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DOI: 10.4230/LIPIcs.ESA.2022.84

An Empirical Evaluation of k-Means Coresets

Authors: Chris Schwiegelshohn and Omar Ali Sheikh-Omar

Published in: LIPIcs, Volume 244, 30th Annual European Symposium on Algorithms (ESA 2022)

Abstract

Coresets are among the most popular paradigms for summarizing data. In particular, there exist many high performance coresets for clustering problems such as k-means in both theory and practice. Curiously, there exists no work on comparing the quality of available k-means coresets. In this paper we perform such an evaluation. There currently is no algorithm known to measure the distortion of a candidate coreset. We provide some evidence as to why this might be computationally difficult. To complement this, we propose a benchmark for which we argue that computing coresets is challenging and which also allows us an easy (heuristic) evaluation of coresets. Using this benchmark and real-world data sets, we conduct an exhaustive evaluation of the most commonly used coreset algorithms from theory and practice.

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Chris Schwiegelshohn and Omar Ali Sheikh-Omar. An Empirical Evaluation of k-Means Coresets. In 30th Annual European Symposium on Algorithms (ESA 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 244, pp. 84:1-84:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{schwiegelshohn_et_al:LIPIcs.ESA.2022.84,
  author =	{Schwiegelshohn, Chris and Sheikh-Omar, Omar Ali},
  title =	{{An Empirical Evaluation of k-Means Coresets}},
  booktitle =	{30th Annual European Symposium on Algorithms (ESA 2022)},
  pages =	{84:1--84:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-247-1},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{244},
  editor =	{Chechik, Shiri and Navarro, Gonzalo and Rotenberg, Eva and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2022.84},
  URN =		{urn:nbn:de:0030-drops-170225},
  doi =		{10.4230/LIPIcs.ESA.2022.84},
  annote =	{Keywords: coresets, k-means coresets, evaluation, benchmark}
}

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An Empirical Evaluation of k-Means Coresets

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