Advertisement

A Novel Self-Adaptive Clustering Algorithm for Dynamic Data

  • Ming Liu
  • Lei Lin
  • Lili Shan
  • Chengjie Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7665)

Abstract

Along with the fast advance of internet technique, internet users have to deal with novel data every day. For most of them, one of the most useful knowledge exploited from web is about the transfer of the information expressed by dynamically updated data. Unfortunately, traditional algorithms often cluster novel data without considering the existent clustering model. They have to cluster input data over again, once input data are updated. Hence, they are time-consuming and inefficient. For efficiently clustering dynamic data, a novel Self-Adaptive Clustering algorithm (abbreviated as SAC) is proposed in this paper. SAC comes from Self Organizing Mapping algorithm (abbreviated as SOM), whereas, it doesn’t need to make any assumption about neuron topology beforehand. Besides, when input data are updated, its topology remodels meanwhile. Experiment results demonstrate that SAC can automatically tune its topology along with the update of input data.

Keywords

Self-adaptive algorithm Competitive learning Minimum spanning tree Self-organizing-mapping 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Martin, S., Detlef, N.: Towards the Automation of Intelligent Data Analysis. Appl. Soft Comput. 6, 348–356 (2006)CrossRefGoogle Scholar
  2. 2.
    Dhillon, I.S., Guan, Y.Q., Kogan, J.: Iterative Clustering of High Dimensional Text Data Augmented by Local Search. In: Proceedings of the Second IEEE International Conference on Data Mining, pp. 131–138. IEEE Press, Japan (2002)Google Scholar
  3. 3.
    Ghaseminezhad, M.H., Karami, A.: A Novel Self-Organizing Map (SOM) Neural Network for Discrete Groups of Data Clustering. Appl. Soft Comput. 11, 3771–3778 (2011)CrossRefGoogle Scholar
  4. 4.
    Melody, Y.K.: Extending the Kohonen Self-Organizing Map Networks for Clustering Analysis. Comput. Stat. Data Anal. 38, 161–180 (2001)zbMATHCrossRefGoogle Scholar
  5. 5.
    Tseng, C.L., Chen, Y.H., Xu, Y.Y., Pao, H.T., Fu, H.C.: A Self-Growing Probabilistic Decision-Based Neural Network with Automatic Data Clustering. Neurocomput. 61, 21–38 (2004)CrossRefGoogle Scholar
  6. 6.
    Melody, Y.K.: Extending the Kohonen Self-Organizing Map Networks for Clustering Analysis. Comput. Stat. Data Anal. 38, 161–180 (2001)zbMATHCrossRefGoogle Scholar
  7. 7.
    Lee, S., Kim, G., Kim, S.: Self-Adaptive and Dynamic Clustering for Online Anomaly Detection. Expert Syst. Appl. 38, 14891–14898 (2011)CrossRefGoogle Scholar
  8. 8.
    Hodge, V.J., Austin, J.: Hierarchical Growing Cell Structures: TreeGCS. IEEE Trans. Knowl. Data Engin. 13, 207–218 (2001)CrossRefGoogle Scholar
  9. 9.
    Duan, L., Xu, L.D., Guo, F., Lee, J., Yan, B.P.: A Local-Density Based Spatial Clustering Algorithm with Noise. Inform. Syst. 32, 978–986 (2007)CrossRefGoogle Scholar
  10. 10.
    Ezequiel, L.R.: Probabilistic Self-Organizing Maps for Qualitative Data. Neural Networks 23, 1208–1225 (2010)CrossRefGoogle Scholar
  11. 11.
    Tokunaga, K., Furukawa, T.: Modular Network SOM. Neural Networks 22, 82–90 (2009)CrossRefGoogle Scholar
  12. 12.
    Kohonen, T., Kaski, S., Lagus, K., Salojarvi, J., Paatero, V., Saarela, A.: Self Organization of a Massive Document Collection. IEEE Trans. Neural Networks 11, 574–585 (2000)CrossRefGoogle Scholar
  13. 13.
    Blake, C., Keogh, E., Merz, C.J.: UCI Repository of Machine Learning Databases. University of California, Irvine (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
  14. 14.
    Alahakoon, D., Halganmuge, S.K., Srinivasan, B.: Dynamic Self-Organizing Maps with Controlled Growth for Knowledge Discovery. IEEE Trans. Neural Networks 11, 601–614 (2000)CrossRefGoogle Scholar
  15. 15.
    Rauber, A., Merkl, D., Dittenbach, M.: The Growing Hierarchical Self-Organizing Map: Exploratory Analysis of High-Dimensional Data. IEEE Trans. Neural Networks 13, 1331–1341 (2002)CrossRefGoogle Scholar
  16. 16.
    Qin, A.K., Suganthan, P.N.: Robust Growing Neural Gas Algorithm with Application in Cluster Analysis. Neural Networks 17, 1135–1148 (2004)zbMATHGoogle Scholar
  17. 17.
    Kohonen, T.: Self-Organizing Maps. Springer, Berlin (1995); (2nd Extended Edition 1997)Google Scholar
  18. 18.
    Robert, L.K., Warwick, K.: The Plastic Self Organising Map. In: Proceedings of the 2002 International Joint Conference on Neural Networks, pp. 727–732. IEEE Press, Hawaii (2002)Google Scholar
  19. 19.
    Hung, C., Wermter, S.: A Dynamic Adaptive Self-Organising Hybrid Model for Text Clustering. In: Proceedings of the Third IEEE International Conference on Data Mining, pp. 75–82. IEEE Press, Melbourne (2003)CrossRefGoogle Scholar
  20. 20.
    Gu, M., Zha, H., Ding, C., He, X.: Simon, H., Xia, J.: Spectral Relaxation Models and Structure Analysis for K-Way Graph Clustering and Bi-Clustering. Technical Report, CSE-01-007, Penn State University (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ming Liu
    • 1
  • Lei Lin
    • 1
  • Lili Shan
    • 1
  • Chengjie Sun
    • 1
  1. 1.MOE-MS Key Laboratory of Natural Language Processing and Speech, School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina

Personalised recommendations