Fully Dynamic k-Center Clustering with Outliers

  • T. H.Hubert Chan
  • , Silvio Lattanzi
  • , Mauro Sozio
  • , Bo Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We consider the robust version of the classic k-center clustering problem, where we wish to remove up to z points (outliers), so as to be able to cluster the remaining points in k clusters with minimum maximum radius. We study such a problem under the fully dynamic adversarial model, where points can be inserted or deleted arbitrarily. In this setting, the main goal is to design algorithms that maintain a high quality solution at any point in time, while requiring a “small” amortized cost, i.e. a “small” number of operations per insertion or deletion, on average. In our work, we provide the first constant bi-criteria approximation algorithm for such a problem with its amortized cost being independent of both z and the size of the current input.

Original languageEnglish
Title of host publicationComputing and Combinatorics - 28th International Conference, COCOON 2022, Proceedings
EditorsYong Zhang, Dongjing Miao, Rolf Möhring
PublisherSpringer Science and Business Media Deutschland GmbH
Pages150-161
Number of pages12
ISBN (Print)9783031221040
DOIs
Publication statusPublished - 1 Jan 2022
Externally publishedYes
Event28th International Conference on Computing and Combinatorics, COCOON 2022 - Shenzhen, China
Duration: 22 Oct 202224 Oct 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13595 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference28th International Conference on Computing and Combinatorics, COCOON 2022
Country/TerritoryChina
CityShenzhen
Period22/10/2224/10/22

Keywords

  • Approximation algorithm
  • Clustering
  • Fully dynamic

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