Approximation algorithms for two variants of correlation clustering problem

Sai Ji (), Dachuan Xu (), Min Li () and Yishui Wang ()
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Sai Ji: Beijing University of Technology
Dachuan Xu: Beijing University of Technology
Min Li: Shandong Normal University
Yishui Wang: Chinese Academy of Sciences

Journal of Combinatorial Optimization, 2022, vol. 43, issue 5, No 2, 933-952

Abstract: Abstract Correlation clustering problem is a clustering problem which has many applications such as protein interaction networks, cross-lingual link detection, communication networks, and social computing. In this paper, we introduce two variants of correlation clustering problem: correlation clustering problem on uncertain graphs and correlation clustering problem with non-uniform hard constrained cluster sizes. Both problems overcome part of the limitations of the existing variants of correlation clustering problem and have practical applications in the real world. We provide a constant approximation algorithm and two approximation algorithms for the former and the latter problem, respectively.

Keywords: Correlation clustering; Uncertain graphs; Non-uniform hard constrained cluster sizes; Approximation algorithm (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10878-020-00612-1

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