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.
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© 2009 Springer-Verlag Berlin Heidelberg
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Palomo, E.J., Domínguez, E., Luque, R.M., Muñoz, J. (2009). Spam Detection Based on a Hierarchical Self-Organizing Map. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence. ICIC 2009. Lecture Notes in Computer Science(), vol 5755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04020-7_4
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DOI: https://doi.org/10.1007/978-3-642-04020-7_4
Publisher Name: Springer, Berlin, Heidelberg
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