Clustering Massive High Dimensional Data with Dynamic Feature Maps

  • Rasika Amarasiri
  • Damminda Alahakoon
  • Kate Smith-Miles
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)


This paper presents an algorithm based on the Growing Self Organizing Map (GSOM) called the High Dimensional Growing Self Organizing Map with Randomness (HDGSOMr) that can cluster massive high dimensional data efficiently. The original GSOM algorithm is altered to accommodate for the issues related to massive high dimensional data. These modifications are presented in detail with experimental results of a massive real-world dataset.


Learning Rate Boundary Node Spread Factor Growth Threshold Massive Dataset 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kohonen, T.: Self Organized formation of Topological Correct Feature Maps. Biological Cybernetics 43, 59–69 (1982)MATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Kohonen, T.: Self Organizing Maps, 3rd edn. Springer, Heidelberg (2001)MATHGoogle Scholar
  3. 3.
    Su, M.-C., Liu, T.-K., Chang, H.-T.: Improving the Self-Organizing Feature Map Algorithm Using an Efficient Initialization Scheme. Tamkang Journal of Science and Engineering 5(1), 35–48 (2002)Google Scholar
  4. 4.
    Alahakoon, D., Halgamuge, S.K., Sirinivasan, B.: Dynamic Self Organizing Maps With Controlled Growth for Knowledge Discovery. IEEE Transactions on Neural Networks, Special Issue on Knowledge Discovery and Data Mining 11(3), 601–614 (2000)Google Scholar
  5. 5.
    Fritzke, B.: Growing cell structures – a self-organizing network for unsupervised and supervised learning. Neural Networks 7(9), 1441–1460 (1994)CrossRefGoogle Scholar
  6. 6.
    Fritzke, B.: Growing grid-a self-organizing network with constant neighborhood range and adaptation strength. Neural Processing Letters 2(5), 9–13 (1995)CrossRefGoogle Scholar
  7. 7.
    Fritzke, B.: A growing neural gas network learns topologies. In: Tesauro, G., Touretzky, D.S., Leen, T.K. (eds.) Advances in Neural Information Processing Systems 7, pp. 625–632. MIT Press, Cambridge (1995)Google Scholar
  8. 8.
    Rauber, A., Merkl, D., Dittenbach, M.: The Growing Hierarchical Self-Organizing Map: Exploratory Analysis of High Dimensional Data. IEEE Transactions on Neural Networks 13(6), 1331–1341 (2002)CrossRefGoogle Scholar
  9. 9.
    Kaski, S.: Fast winner search for SOM-based monitoring and retrieval of high-dimensional data. In: Artificial Neural Networks, 1999. ICANN 1999. Ninth International Conference on (Conf. Publ. No. 470), IEE (1999)Google Scholar
  10. 10.
    Kohonen, T.: Fast Evolutionary Learning with Batch-Type Self-Organizing Maps. Neural Processing Letters 9(2), 153–162 (1999)CrossRefGoogle Scholar
  11. 11.
    Kaski, S., et al.: WEBSOM- Self Organizing maps of document collections. Neurocomputing 21, 101–117 (1998)MATHCrossRefGoogle Scholar
  12. 12.
    Amarasiri, R., et al.: Enhancing Clustering Performance of Feature Maps Using Randomness. In: Workshop on Self Organizing Maps (WSOM), Paris, France (2005)Google Scholar
  13. 13.
    Amarasiri, R., et al.: HDGSOMr: A High Dimensional Growing Self Organizing Map Using Randomness for Efficient Web and Text Mining. In: IEEE/ACM/WIC Conference on Web Intelligence (WI), Paris, France (2005)Google Scholar
  14. 14.
    Wikipedia, Randomness (2005)Google Scholar
  15. 15.
    Holland, J.: Genetic algorithms and the optimal allocations of trials. SIAM Journal of Computing 2(2), 88–105 (1973)MATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    Kirkpatrick, S., Gelatt Jr., C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220(4598), 671–680 (1983)CrossRefMathSciNetGoogle Scholar
  17. 17.
    Amarasiri, R., Ceddia, J., Alahakoon, D.: Exploratory Data Mining Lead by Text Mining Using a Novel High Dimensional Clustering Algorithm. In: International Conference on Machine Learning and Applications, IEEE, LA, USA (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Rasika Amarasiri
    • 1
  • Damminda Alahakoon
    • 1
  • Kate Smith-Miles
    • 2
  1. 1.Clayton School of Information TechnologyMonash UniversityAustralia
  2. 2.School of Engineering and Information TechnologyDeakin UniversityBurwoodAustralia

Personalised recommendations