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 general...

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Vydáno v:Journal of computer and system sciences Ročník 130; s. 43 - 70
Hlavní autoři: Deng, Shichuan, Li, Jian, Rabani, Yuval
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 01.12.2022
Témata:
Approximation algorithms
Clustering
Dynamic points
Facility location
Multi-objective optimization
Facility location
Approximation algorithms
Multi-objective optimization
Clustering
Dynamic points
ISSN:0022-0000, 1090-2724
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Shrnutí: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.
ISSN:0022-0000
1090-2724
DOI:10.1016/j.jcss.2022.07.001