Greedy Strategy Works for k-Center Clustering with Outliers and Coreset Construction
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Greedy Strategy Works for k-Center Clustering with Outliers and Coreset Construction

Authors Hu Ding, Haikuo Yu, Zixiu Wang

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Author Details

Hu Ding
  • School of Computer Science and Technology, University of Science and Technology of China, China
Haikuo Yu
  • School of Computer Science and Technology, University of Science and Technology of China, China
Zixiu Wang
  • School of Computer Science and Technology, University of Science and Technology of China, China

Funding

  • Ding, Hu: this work was supported in part by NSF through grant CCF-1656905.

Acknowledgements

The authors want to thank the anonymous reviewers for their helpful comments and suggestions for improving the paper.

Cite As Get BibTex

Hu Ding, Haikuo Yu, and Zixiu Wang. Greedy Strategy Works for k-Center Clustering with Outliers and Coreset Construction. In 27th Annual European Symposium on Algorithms (ESA 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 144, pp. 40:1-40:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019) https://doi.org/10.4230/LIPIcs.ESA.2019.40

Abstract

We study the problem of k-center clustering with outliers in arbitrary metrics and Euclidean space. Though a number of methods have been developed in the past decades, it is still quite challenging to design quality guaranteed algorithm with low complexity for this problem. Our idea is inspired by the greedy method, Gonzalez’s algorithm, for solving the problem of ordinary k-center clustering. Based on some novel observations, we show that this greedy strategy actually can handle k-center clustering with outliers efficiently, in terms of clustering quality and time complexity. We further show that the greedy approach yields small coreset for the problem in doubling metrics, so as to reduce the time complexity significantly. Our algorithms are easy to implement in practice. We test our method on both synthetic and real datasets. The experimental results suggest that our algorithms can achieve near optimal solutions and yield lower running times comparing with existing methods.

Subject Classification

ACM Subject Classification
  • Theory of computation → Approximation algorithms analysis
Keywords
  • k-center clustering
  • outliers
  • coreset
  • doubling metrics
  • random sampling

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