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An improved primal-dual approximation algorithm for the k-means problem with penalties

Published online by Cambridge University Press:  16 August 2021

Chunying Ren ,
Dachuan Xu ,
Donglei Du  and
Min Li Open the ORCID record for Min Li [Opens in a new window]
Chunying Ren
Affiliation:
Department of Operations Research and Information Engineering, Beijing University of Technology, Beijing 100124, P.R. China
Dachuan Xu
Affiliation:
Department of Operations Research and Information Engineering, Beijing University of Technology, Beijing 100124, P.R. China
Donglei Du
Affiliation:
Faculty of Management, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
Min Li*
Affiliation:
School of Mathematics and Statistics, Shandong Normal University, Jinan 250014, P.R. China
*
*Corresponding author. Email: liminemily@sdnu.edu.cn
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Abstract

In the k-means problem with penalties, we are given a data set $${\cal D} \subseteq \mathbb{R}^\ell $$ of n points where each point $$j \in {\cal D}$$ is associated with a penalty cost pj and an integer k. The goal is to choose a set $${\rm{C}}S \subseteq {{\cal R}^\ell }$$ with |CS| ≤ k and a penalized subset $${{\cal D}_p} \subseteq {\cal D}$$ to minimize the sum of the total squared distance from the points in D / Dp to CS and the total penalty cost of points in Dp, namely $$\sum\nolimits_{j \in {\cal D}\backslash {{\cal D}_p}}{d^2}(j,{\rm{C}}S) + \sum\nolimits_{j \in {{\cal D}_p}}{p_j}$$. We employ the primal-dual technique to give a pseudo-polynomial time algorithm with an approximation ratio of (6.357+ε) for the k-means problem with penalties, improving the previous best approximation ratio 19.849+∊ for this problem given by Feng et al. in Proceedings of FAW (2019).

Keywords

K-means problem with penaltieslinear programapproximation algorithmJV algorithm

Information

Type
Special Issue: Theory and Applications of Models of Computation (TAMC 2020)
Information
Mathematical Structures in Computer Science , Volume 32 , Issue 2: Theory and Applications of Models of Computation (TAMC 2020) , February 2022 , pp. 151 - 163
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Copyright
© The Author(s), 2021. Published by Cambridge University Press

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Footnotes

†A preliminary version of this paper appeared in Proceedings of the 16th Annual Conference on Theory and Applications of Models of Computation, pp. 377–389, 2020

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