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Contraction Methods for Correlation Clustering: The Order is Important

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Recent Advances in Computational Optimization

Part of the book series: Studies in Computational Intelligence ((SCI,volume 795))

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Abstract

Correlation clustering is a NP-hard problem, and for large graphs finding even just a good approximation of the optimal solution is a hard task. In previous articles we have suggested a contraction method and its divide and conquer variant. In this article we examine the effect of executing the steps of the contraction method in a different order.

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References

  1. Aszalós, L., Bakó, M.: Advanced search methods (in Hungarian). http://www.tankonyvtar.hu/hu/tartalom/tamop412A/2011-0103_13_fejlett_keresoalgoritmusok (2012)

  2. Aszalós, L., Bakó, M.: Correlation clustering: divide and conquer. In: Ganzha, M., Maciaszek, L., Paprzycki, M. (eds.) Position Papers of the 2016 Federated Conference on Computer Science and Information Systems, Annals of Computer Science and Information Systems, vol. 9, pp. 73–78. PTI (2016). https://doi.org/10.15439/2016F168

  3. Aszalós, L., Mihálydeák, T.: Rough clustering generated by correlation clustering. In: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, pp. 315–324. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1109/TKDE.2007.1061

    Article  Google Scholar 

  4. Aszalós, L., Mihálydeák, T.: Rough classification based on correlation clustering. In: Rough Sets and Knowledge Technology, pp. 399–410. Springer (2014). https://doi.org/10.1007/978-3-319-11740-9_37

    MATH  Google Scholar 

  5. Aszalós, L., Mihálydeák, T.: Correlation clustering by contraction, a more effective method. In: Recent Advances in Computational Optimization, vol. 655, pp. 81–95. Springer (2016). https://doi.org/10.1007/978-3-319-40132-4_6

    Chapter  Google Scholar 

  6. Bansal, N., Blum, A., Chawla, S.: Correlation clustering. Mach. Learn. 56(1–3), 89–113 (2004). https://doi.org/10.1023/B:MACH.0000033116.57574.95

    Article  MathSciNet  Google Scholar 

  7. Bhattacharya, A., De, R.K.: Divisive correlation clustering algorithm (dcca) for grouping of genes: detecting varying patterns in expression profiles. Bioinformatics 24(11), 1359–1366 (2008). https://doi.org/10.1093/bioinformatics/btn133

    Article  Google Scholar 

  8. Chen, Z., Yang, S., Li, L., Xie, Z.: A clustering approximation mechanism based on data spatial correlation in wireless sensor networks. In: Wireless Telecommunications Symposium (WTS), 2010, pp. 1–7. IEEE (2010). https://doi.org/10.1109/WTS.2010.5479626

  9. DuBois, T., Golbeck, J., Kleint, J., Srinivasan, A.: Improving recommendation accuracy by clustering social networks with trust. Recommender Systems & the Social Web 532, 1–8 (2009). https://doi.org/10.1145/2661829.2662085

  10. Kim, S., Nowozin, S., Kohli, P., Yoo, C.D.: Higher-order correlation clustering for image segmentation. In: Advances in Neural Information Processing Systems, pp. 1530–1538 (2011). DOI 10.1.1.229.4144

    Google Scholar 

  11. Néda, Z., Florian, R., Ravasz, M., Libál, A., Györgyi, G.: Phase transition in an optimal clusterization model. Physica A: Stat. Mech. Appl. 362(2), 357–368 (2006). https://doi.org/10.1016/j.physa.2005.08.008

    Article  Google Scholar 

  12. Yang, B., Cheung, W.K., Liu, J.: Community mining from signed social networks. Knowl. Data Eng. IEEE Trans. 19(10), 1333–1348 (2007)

    Article  Google Scholar 

  13. Zahn Jr, C.: Approximating symmetric relations by equivalence relations. J. Soc. Ind. Appl. Math. 12(4), 840–847 (1964). https://doi.org/10.1137/0112071

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Correspondence to László Aszalós .

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Aszalós, L., Bakó, M. (2019). Contraction Methods for Correlation Clustering: The Order is Important. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. Studies in Computational Intelligence, vol 795. Springer, Cham. https://doi.org/10.1007/978-3-319-99648-6_1

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