Advertisement

Self-Organizing-Map-Based Metamodeling for Massive Text Data Exploration

  • Kin Keung Lai
  • Lean Yu
  • Ligang Zhou
  • Shouyang Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)

Abstract

In this study, we describe the use of the self-organizing map (SOM) as a metamodeling technique to design a parallel text data exploration system. Firstly, the large textual collections are divided into various small data subsets. Based on the different subsets, different unitary SOM models, i.e., base models, are then trained for word clustering map. In this phase, different SOM models are implemented in parallel to gain greater computational efficiency. Finally, a SOM-based metamodel can be produced to formulate a text category map through learning from all base models. For illustration the proposed metamodel is applied to a massive text data collection.

Keywords

Text Document Data Partition Textual Collection Text Feature Vector Metamodeling Technique 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kohonen, T.: Self-Organizing Maps. Springer, Berlin (1995)Google Scholar
  2. 2.
    Kaski, S., Honkela, T., Lagus, K., Kohonen, T.: WEBSOM — Self-organizing Maps of Document Collections. Neurocomputing 21, 101–117 (1998)MATHCrossRefGoogle Scholar
  3. 3.
    Kohonen, T., Kaski, S., Lagus, K., Salojarvi, J., Honkela, J., Paatero, V., Saarela, A.: Self Organization of a Massive Document Collection. IEEE Transactions on Neural Networks 11, 574–585 (2000)CrossRefGoogle Scholar
  4. 4.
    Lee, C.H., Yang, H.C.: A Multilingual Text Mining Approach Based on Self-Organizing Maps. Applied Intelligence 18, 295–310 (2003)MATHCrossRefGoogle Scholar
  5. 5.
    Deerwester, S., Dumais, S., Furnas, G., Landauer, K.: Indexing by Latent Semantic Analysis. Journal of the American Society for Information Science 41, 391–407 (1990)CrossRefGoogle Scholar
  6. 6.
    Kleinrock, L., Huang, J.H.: On Parallel Processing Systems: Amdahl’s Law Generalized and Some Results on Optimal Design. IEEE Transactions on Software Engineering 18, 434–447 (1992)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kin Keung Lai
    • 1
    • 2
  • Lean Yu
    • 2
    • 3
  • Ligang Zhou
    • 2
  • Shouyang Wang
    • 1
    • 3
  1. 1.College of Business AdministrationHunan UniversityChangshaChina
  2. 2.Department of Management SciencesCity University of Hong KongHong Kong
  3. 3.Institute of Systems ScienceAcademy of Mathematics and Systems Science, Chinese Academy of SciencesBeijingChina

Personalised recommendations