Advertisement

A Three-Way Decisions Approach to “”Density-Based Overlapping Clustering

  • Hong YuEmail author
  • Ying Wang
  • Peng Jiao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8449)

Abstract

Most of clustering methods assume that each object must be assigned to exactly one cluster, however, overlapping clustering is more appropriate than crisp clustering in a variety of important applications such as the network structure analysis and biological information. This paper provides a three-way decisions approach for overlapping clustering based on the decision-theoretic rough set model, where each cluster is described by an interval set which is defined by a pair of sets called the lower and upper bounds, and the overlapping objects usually are distributed in the region between the lower and upper regions. Besides, a density-based clustering algorithm is proposed using the approach considering the advantages of the density-based clustering algorithms in finding the arbitrary shape clusters. The results of comparison experiments show that the three-way decisions approach is not only effective to overlapping clustering but also good at discovering the arbitrary shape clusters.

Keywords

Overlapping clustering Three-way decisions Decision-theoretic rough set theory Density-based clustering Data mining 

Notes

Acknowledgments

This work was supported in part by the China NSFC grant (No.61379114 & No.61272060).

References

  1. 1.
    Asharaf, S., Murty, M.N.: An adaptive rough fuzzy single pass algorithm for clustering large data sets. Pattern Recogn. 36(12), 3015–3018 (2003)CrossRefGoogle Scholar
  2. 2.
    Aydin, N., Naït-Abdesselam, F., Pryyma, V., Turgut, D.: Overlapping clusters algorithm in ad hoc networks. In: 2010 IEEE Global Telecommunications Conference, pp. 1–5 (2010)Google Scholar
  3. 3.
  4. 4.
    Chen, M., Miao, D.Q.: Interval set clustering. Expert Syst. Appl. 38, 2923–2932 (2011)CrossRefGoogle Scholar
  5. 5.
    Duan, L., Xu, L.D., Guo, F., Lee, J., Yan, B.P.: A local-density based spatial clustering algorithm with noise. Inf. Syst. 32(7), 978–986 (2007)CrossRefGoogle Scholar
  6. 6.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.W.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD (1996)Google Scholar
  7. 7.
    Fu, Q., Banerjee, A.: Multiplicative mixture models for overlapping clustering. In: IEEE International Conference on Data Mining, pp. 791–797 (2003)Google Scholar
  8. 8.
    Herbert, J.P., Yao, J.T.: Learning optimal parameters in decision-theoretic rough sets. In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds.) RSKT 2009. LNCS (LNAI), vol. 5589, pp. 610–617. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Lingras, P., Bhalchandra, P., Khamitkar, S., Mekewad, S., Rathod, R.: Crisp and soft clustering of mobile calls. In: Sombattheera, C., Agarwal, A., Udgata, S.K., Lavangnananda, K. (eds.) MIWAI 2011. LNCS (LNAI), vol. 7080, pp. 147–158. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Lingras, P., Yao, Y.Y.: Time complexity of rough clustering: GAs versus K-means. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 263–270. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  11. 11.
    Liu, D., Yao, Y.Y., Li, T.R.: Three-way investment decisions with decision-theoretic rough sets. Int. J. Comput. Intell. Syst. 4(1), 66–74 (2011)CrossRefGoogle Scholar
  12. 12.
    Ma, S., Wang, T.J., Tang, S.W., Yang, D.Q., Gao, J.: A fast clustering algorithm based on reference and density. J. Softw. 14(6), 1089–1095 (2003)zbMATHGoogle Scholar
  13. 13.
    Obadi, G., Dráždilová, P., Hlaváček, L., Martinovič, J., Snášel, V.: A tolerance rough set based overlapping clustering for the DBLP data. In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp. 57–60 (2010)Google Scholar
  14. 14.
    Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11(5), 341–356 (1982)CrossRefGoogle Scholar
  15. 15.
    Pawlak, Z.: Rough classification. Int. J. Man-Mach. Stud. 20(5), 469–483 (1984)CrossRefGoogle Scholar
  16. 16.
    Ren, Y., Liu, X.D., Liu, W.Q.: DBCAMM: a novel density based clustering algorithm via using the Mahalanobis metric. Appl. Soft Comput. 12(5), 1542–1554 (2010)CrossRefGoogle Scholar
  17. 17.
    Sander, J., Ester, M., Kriegel, H.P., Xu, X.W.: Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications. Data Min. Knowl. Disc. 2(2), 169–194 (1998)CrossRefGoogle Scholar
  18. 18.
    Subramaniam, S., Palpanas, T., Papadopoulos, D., Kalogeraki, V., Gunopulos, D.: Online outlier detection in sensor data using non-parametric models. In: Proceedings of the 32nd International Conference on Very Large Data Bases, pp. 187–198 (2006)Google Scholar
  19. 19.
    Takaki, M.: A extraction method of overlapping cluster based on network structure analysis. In: IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, pp. 212–217 (2007)Google Scholar
  20. 20.
    Tsai, C.-F., Liu, C.-W.: KIDBSCAN: a new efficient data clustering algorithm. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 702–711. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  21. 21.
    Weller, A.C.: Editorial Peer Review: Its Strengths & Weaknesses. Information Today Inc., Medford (2001)Google Scholar
  22. 22.
    Wu, X.H., Zhou, J.J.: Possibilistic fuzzy c-means clustering model using kernel methods. In: Proceeding of the 2005 International Conference on Computational Intelligence for Modelling, Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’05), vol. 2, pp. 465–470 (2005)Google Scholar
  23. 23.
    Yao, Y.Y.: Three-way decisions with probabilistic rough sets. Inf. Sci. 180(3), 341–353 (2010)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Yao, Y.Y.: The superiority of three-way decisions in probabilistic rough set models. Inf. Sci. 181(6), 1080–1096 (2011)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Yao, Y.Y., Lingras, P., Wang, R.Z., Miao, D.Q.: Interval set cluster analysis: a re-formulation. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds.) RSFDGrC 2009. LNCS (LNAI), vol. 5908, pp. 398–405. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  26. 26.
    Yao, Y.Y., Wong, S.K.M.: A decision theoretic framework for approximating concepts. Int. J. Man-Mach. Stud. 37(6), 793–809 (1992)CrossRefGoogle Scholar
  27. 27.
    Yousri, N.A., Kamel, M.S., Ismail, M.A.: A possibilistic density based clustering for discovering clusters of arbitrary shapes and densities in high dimensional data. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012, Part III. LNCS, vol. 7665, pp. 577–584. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  28. 28.
    Yu, H., Luo, H.: A novel possibilistic fuzzy leader clustering algorithm. Int. J. Hybrid Intell. Syst. 8(1), 31–40 (2011)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Zhou, B., Yao, Y.Y., Luo, J.G.: A three-way decision approach to email spam filtering. In: Farzindar, A., Kešelj, V. (eds.) Canadian AI 2010. LNCS (LNAI), vol. 6085, pp. 28–39. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  30. 30.
    Lusseau, D., Newman, M.E.J.: Identifying the role that animals play in their social networks. Proc. R. Soc. Lond. Ser. B Biol. Sci. 271(Suppl 6), S477–S481 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Chongqing Key Laboratory of Computational IntelligenceChongqing University of Posts and TelecommunicationsChongqingChina

Personalised recommendations