Advertisement

Ant Clustering Using Ensembles of Partitions

  • Yuhua Gu
  • Lawrence O. Hall
  • Dmitry B. Goldgof
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5519)

Abstract

Classical clustering algorithms require a predefined number of cluster centers. They are often very sensitive to initialization, which can result in very different clustering results. We present a two-phase algorithm which is a combination of a new ant based algorithm and a nonnegative matrix factorization-based consensus clustering algorithm. Ant clustering approaches can and do find the number of clusters as well as the data partition. However, they are very sensitive to both initial conditions and select parameters. Here, we show that using an ensemble of ant partitions and NMF to combine them we can find both the “right” number of clusters and a good data partition. Experiments were done with ten data sets. We conducted a wide range of comparisons that demonstrate the effectiveness of this new approach.

Keywords

Clustering Ant-based clustering Fuzzy c-means Consensus clustering NMF Ensembles 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hall, L.O., Ozyurt, I.B., Bezdek, J.C.: Clustering with a Genetically Optimized Approach. IEEE Transactions on Evolutionary Computation 3(2), 103–112 (1999)CrossRefGoogle Scholar
  2. 2.
    Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn., pp. 111–114. Prentice Hall, Upper Saddle River (2003)zbMATHGoogle Scholar
  3. 3.
    Dorigo, M., Stutzle, T.: Ant Colony Optimization. Prentice Hall of India Private Limited, New Delhi (2005)zbMATHGoogle Scholar
  4. 4.
    Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: Optimization by a colony of cooperating agents. IEEE Transaction on SMC-part B 26(1), 29–41 (1996)Google Scholar
  5. 5.
    Deneubourg, J.L., Goss, S., Franks, N., Sendova-Franks, A.: Detrain: The dynamics of collective sorting: robot-like ants and ant-like robots. In: Meyer, J.A., Wilson, S. (eds.) Proc. of the first Intern. Conf. on Simulation of Adaptive Behaviour: From Animals to Animats 1, pp. 356–365. MIT Press, Cambridge (1991)Google Scholar
  6. 6.
    Lumer, E., Faieta, B.: Diversity and Adaptation in Populations of Clustering Ants. In: Proc. of the third Interm. Conf. on Simulation of Adaptive Behavior: from Animals to Animats 3, pp. 501–508. MIT Press, Cambridge (1994)Google Scholar
  7. 7.
    Handl, J., Knowles, J., Dorigo, M.: Strategies for the Increased Robustness of Ant-based clustering. In: Di Marzo Serugendo, G., Karageorgos, A., Rana, O.F., Zambonelli, F. (eds.) ESOA 2003. LNCS, vol. 2977, pp. 90–104. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Labroche, N., Monmarche, N., Venturini, G.: A New Clustering Algorithm Based on the Chemical Recognition System of Ants. In: van Harmelen, F. (ed.) Proc. of the 15th European Conference on Artificial Intelligence, Lyon, France, pp. 345–349 (2002)Google Scholar
  9. 9.
    Fern, X.Z., Brodley, C.E.: Solving cluster ensemble problems by bipartite graph partitioning. In: ICML (2004)Google Scholar
  10. 10.
    Gionis, A., Mannila, H., Tsaparas, P.: Clustering aggregation. In: ICDE, pp. 341–352 (2005)Google Scholar
  11. 11.
    Hu, X., Yoo, I., Zhang, X., Nanavati, P., Das, D.: Wavelet transformation and cluster ensemble for gene expression analysis. International Journal of Bioinformatics Research and Application 1(4), 447–460 (2006)CrossRefGoogle Scholar
  12. 12.
    Li, T., Ding, C.: Weighted Consensus Clustering. In: Proceedings of the 2008 SIAM International Conference on Data Mining, Atlanta, April 24-26 (2008)Google Scholar
  13. 13.
    Ding, C., Li, T., Peng, W., Park, H.: Orthogonal nonnegative matrix tri-factorizations for clustering. In: SIGKDD, pp. 126–135 (2006)Google Scholar
  14. 14.
    Strehl, A., Ghosh, J.: Cluster ensembles - a knowledge reuse framework for combining multiple partitions. Journal on Machine Learning Research (JMLR) 3, 583–617 (2002)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Lee, D.D., Seung, H.S.: Learning the parts of objects with nonnegative matrix factorization. Nature 401, 788–791 (1999)CrossRefGoogle Scholar
  16. 16.
    Xu, W., et al.: Document Clustering Based on Non-Negative Matrix Factorization. In: SIGIR, pp. 267–273 (2003)Google Scholar
  17. 17.
    Blake, C.L., Newman, D.J., Hettich, S., Merz, C.J.: UCI repository of machine learning databases (1998)Google Scholar
  18. 18.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From natural to artificial systems. Oxford University Press, New York (1999)zbMATHGoogle Scholar
  19. 19.
    Hall, L.O., Kanade, P.M.: Swarm Based Fuzzy Clustering with partition Validity. In: FUZZ 2005. The 14th IEEE International Conference on Fuzzy Systems, 2005, May 22-25, pp. 991–995 (2005)Google Scholar
  20. 20.
    Zaharie, D., Zamfirache, F.: Dealing with noise in ant-based clustering. In: Proc. IEEE Congress of Evolutionary Computation 2005, Edinburgh, pp. 2395–2402 (2005)Google Scholar
  21. 21.
    Brunet, J., Tamayo, P., Golub, T., Mesirov, J.: Metagenes and molecular pattern discovery using matrix factorization. Proceedings of the National Academy of Sciences 101(12), 4164–4169 (2004)CrossRefGoogle Scholar
  22. 22.
    Kurita, T.: An efficient agglomerative clustering algorithm for region growing. In: Proc. of IAPR Workshop on Machine Vision Applications, pp. 210–213 (1994)Google Scholar
  23. 23.
    Gu, Y., Hall, L.O., Goldgof, D.B.: Ant Clustering with consensus, Technical Report ISL-1-2009, http://www.cse.usf.edu/~hall/papers/tr109.pdf

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yuhua Gu
    • 1
  • Lawrence O. Hall
    • 1
  • Dmitry B. Goldgof
    • 1
  1. 1.Dept. of Computer Science & EngineeringUniversity of South FloridaTampaU.S.A.

Personalised recommendations