License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ICALP.2021.70
URN: urn:nbn:de:0030-drops-141397
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2021/14139/
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Ganesh, Arun ; Maggs, Bruce M. ; Panigrahi, Debmalya

Universal Algorithms for Clustering Problems

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LIPIcs-ICALP-2021-70.pdf (0.7 MB)

Abstract

This paper presents universal algorithms for clustering problems, including the widely studied k-median, k-means, and k-center objectives. The input is a metric space containing all potential client locations. The algorithm must select k cluster centers such that they are a good solution for any subset of clients that actually realize. Specifically, we aim for low regret, defined as the maximum over all subsets of the difference between the cost of the algorithm’s solution and that of an optimal solution. A universal algorithm’s solution sol for a clustering problem is said to be an (α, β)-approximation if for all subsets of clients C', it satisfies sol(C') ≤ α ⋅ opt(C') + β ⋅ mr, where opt(C') is the cost of the optimal solution for clients C' and mr is the minimum regret achievable by any solution.
Our main results are universal algorithms for the standard clustering objectives of k-median, k-means, and k-center that achieve (O(1), O(1))-approximations. These results are obtained via a novel framework for universal algorithms using linear programming (LP) relaxations. These results generalize to other ?_p-objectives and the setting where some subset of the clients are fixed. We also give hardness results showing that (α, β)-approximation is NP-hard if α or β is at most a certain constant, even for the widely studied special case of Euclidean metric spaces. This shows that in some sense, (O(1), O(1))-approximation is the strongest type of guarantee obtainable for universal clustering.

BibTeX - Entry

@InProceedings{ganesh_et_al:LIPIcs.ICALP.2021.70,
  author =	{Ganesh, Arun and Maggs, Bruce M. and Panigrahi, Debmalya},
  title =	{{Universal Algorithms for Clustering Problems}},
  booktitle =	{48th International Colloquium on Automata, Languages, and Programming (ICALP 2021)},
  pages =	{70:1--70:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-195-5},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{198},
  editor =	{Bansal, Nikhil and Merelli, Emanuela and Worrell, James},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2021/14139},
  URN =		{urn:nbn:de:0030-drops-141397},
  doi =		{10.4230/LIPIcs.ICALP.2021.70},
  annote =	{Keywords: universal algorithms, clustering, k-median, k-means, k-center}
}

Keywords: universal algorithms, clustering, k-median, k-means, k-center
Collection: 48th International Colloquium on Automata, Languages, and Programming (ICALP 2021)
Issue Date: 2021
Date of publication: 02.07.2021

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