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Approximation algorithms for two variants of correlation clustering problem

Author

Listed:
  • Sai Ji

    (Beijing University of Technology)

  • Dachuan Xu

    (Beijing University of Technology)

  • Min Li

    (Shandong Normal University)

  • Yishui Wang

    (Chinese Academy of Sciences)

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.

Suggested Citation

  • Sai Ji & Dachuan Xu & Min Li & Yishui Wang, 0. "Approximation algorithms for two variants of correlation clustering problem," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-20.
    • Handle: RePEc:spr:jcomop:v::y::i::d:10.1007_s10878-020-00612-1
    DOI: 10.1007/s10878-020-00612-1
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    References listed on IDEAS

    as
    1. Min Li & Dachuan Xu & Dongmei Zhang & Tong Zhang, 2019. "A Streaming Algorithm for k-Means with Approximate Coreset," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(01), pages 1-18, February.
    2. Anke van Zuylen & David P. Williamson, 2009. "Deterministic Pivoting Algorithms for Constrained Ranking and Clustering Problems," Mathematics of Operations Research, INFORMS, vol. 34(3), pages 594-620, August.
    3. Aardal, Karen & van den Berg, Pieter L. & Gijswijt, Dion & Li, Shanfei, 2015. "Approximation algorithms for hard capacitated k-facility location problems," European Journal of Operational Research, Elsevier, vol. 242(2), pages 358-368.
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