Approximation Algorithms for Clustering with Dynamic Points

We study two generalizations of classic clustering problems called dynamic ordered \(k\)-median and dynamic \(k\)-supplier, where the points that need clustering evolve over time, and we are allowed to move the cluster centers between consecutive time steps. In these dynamic clustering problems, the...

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Veröffentlicht in:arXiv.org
Hauptverfasser: Deng, Shichuan, Li, Jian, Rabani, Yuval
Format: Paper
Sprache:Englisch
Veröffentlicht: Ithaca Cornell University Library, arXiv.org 25.07.2022
Schlagworte:
Algorithms
Approximation
Clients
Clustering
Mathematical analysis
Metric space
Outliers (statistics)
ISSN:2331-8422
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Zusammenfassung:We study two generalizations of classic clustering problems called dynamic ordered \(k\)-median and dynamic \(k\)-supplier, where the points that need clustering evolve over time, and we are allowed to move the cluster centers between consecutive time steps. In these dynamic clustering problems, the general goal is to minimize certain combinations of the service cost of points and the movement cost of centers, or to minimize one subject to some constraints on the other. We obtain a constant-factor approximation algorithm for dynamic ordered \(k\)-median under mild assumptions on the input. We give a 3-approximation for dynamic \(k\)-supplier and a multi-criteria approximation for its outlier version where some points can be discarded, when the number of time steps is two. We complement the algorithms with almost matching hardness results.
Bibliographie:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
ISSN:2331-8422
DOI:10.48550/arxiv.2006.14403