Advertisement

Contraction Methods for Correlation Clustering: The Order is Important

  • László AszalósEmail author
  • Mária Bakó
Chapter
  • 192 Downloads
Part of the Studies in Computational Intelligence book series (SCI, volume 795)

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.

Keywords

Correlation Clustering Contraction Method Attractive Cluster Relative Tolerance Typical Random Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Aszalós, L., Bakó, M.: Advanced search methods (in Hungarian). http://www.tankonyvtar.hu/hu/tartalom/tamop412A/2011-0103_13_fejlett_keresoalgoritmusok (2012)
  2. 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. 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.2007.1061CrossRefGoogle Scholar
  4. 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_37zbMATHGoogle Scholar
  5. 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_6CrossRefGoogle Scholar
  6. 6.
    Bansal, N., Blum, A., Chawla, S.: Correlation clustering. Mach. Learn. 56(1–3), 89–113 (2004).  https://doi.org/10.1023/B:MACH.0000033116.57574.95MathSciNetCrossRefGoogle Scholar
  7. 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/btn133CrossRefGoogle Scholar
  8. 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. 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. 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 10.1.1.229.4144Google Scholar
  11. 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.2005.08.008CrossRefGoogle Scholar
  12. 12.
    Yang, B., Cheung, W.K., Liu, J.: Community mining from signed social networks. Knowl. Data Eng. IEEE Trans. 19(10), 1333–1348 (2007)CrossRefGoogle Scholar
  13. 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

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of InformaticsUniversity of DebrecenDebrecenHungary
  2. 2.Faculty of Economics at University of DebrecenDebrecenHungary

Personalised recommendations