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Spam Detection Based on a Hierarchical Self-Organizing Map

  • Esteban José Palomo
  • Enrique Domínguez
  • Rafael Marcos Luque
  • José Muñoz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5755)

Abstract

The GHSOM is an artificial neural network that has been widely used for data clustering. The hierarchical architecture of the GHSOM is more flexible than a single SOM since it is adapted to input data, mirroring inherent hierarchical relations among them. The adaptation process of the GHSOM architecture is controlled by two parameters. However, these parameters have to be established in advance and this task is not always easy. In this paper, a new hierarchical self-organizing model that has just one parameter is proposed. The performance of this model has been evaluated by building a spam detector. Experimental results confirm the goodness of this approach.

Keywords

Data clustering hierarchical self-organization spam detection 

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References

  1. 1.
    Cranor, L., LaMacchia, B.: Spam! Commun. ACM 41(8), 74–83 (1998)CrossRefGoogle Scholar
  2. 2.
    Clark, J., Koprinska, I., Poon, J.: A Neural Network Based Approach to Automated E-Mail Classification. In: Proceedings of IEEE/WIC International Conference on Web Intelligence, 2003. WI 2003, pp. 702–705 (2003)Google Scholar
  3. 3.
    Yang, Y., Elfayoumy, S.: Anti-Spam Filtering Using Neural Networks and Bayesian Classifiers. In: International Symposium on Computational Intelligence in Robotics and Automation, 2007. CIRA 2007, pp. 272–278 (2007)Google Scholar
  4. 4.
    Kohonen, T.: Self-Organized Formation of Topologically Correct Feature Maps. Biological cybernetics 43(1), 59–69 (1982)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    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
  6. 6.
    Alahakoon, D., Halgamuge, S., Srinivasan, B.: Dynamic Self-Organizing Maps with Controlled Growth for Knowledge Discovery. IEEE Transactions on Neural Networks 11, 601–614 (2000)CrossRefGoogle Scholar
  7. 7.
    Asuncion, A., Newman, D.: UCI machine learning repository (2007)Google Scholar
  8. 8.
    Dittenbach, M., Rauber, A., Merkl, D.: Recent Advances with the Growing Hierarchical Self-Organizing Map. In: 3rd Workshop on Self-Organising Maps (WSOM), pp. 140–145 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Esteban José Palomo
    • 1
  • Enrique Domínguez
    • 1
  • Rafael Marcos Luque
    • 1
  • José Muñoz
    • 1
  1. 1.Department of Computer Science E.T.S.I. InformaticaUniversity of MalagaMalagaSpain

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