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Algorithms and complexity results for labeled correlation clustering problem

Author

Listed:
  • Xianmin Liu

    (Harbin Institute of Technology)

  • Jianzhong Li

    (Harbin Institute of Technology)

Abstract

The Labeled Correlation Clustering problem, a variant of Correlation Clustering problem, is defined and studied in this paper. It is shown that the problem is NP-complete, and an approximation algorithm is given. For the case when a parameter is fixed, a better approximation algorithm is proposed, and, for a simple fragment of that problem, a PTime algorithm is introduced.

Suggested Citation

  • Xianmin Liu & Jianzhong Li, 2015. "Algorithms and complexity results for labeled correlation clustering problem," Journal of Combinatorial Optimization, Springer, vol. 29(2), pages 488-501, February.
    • Handle: RePEc:spr:jcomop:v:29:y:2015:i:2:d:10.1007_s10878-013-9607-y
    DOI: 10.1007/s10878-013-9607-y
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    References listed on IDEAS

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    1. Peng Zhang & Jin-Yi Cai & Lin-Qing Tang & Wen-Bo Zhao, 2011. "Approximation and hardness results for label cut and related problems," Journal of Combinatorial Optimization, Springer, vol. 21(2), pages 192-208, February.
    2. Refael Hassin & Jérôme Monnot & Danny Segev, 2007. "Approximation algorithms and hardness results for labeled connectivity problems," Journal of Combinatorial Optimization, Springer, vol. 14(4), pages 437-453, November.
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