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Web document clustering based on a hierarchical self-organizing model

Abstract:

In this work, a hierarchical self-organizing model based on the GHSOM is presented in order to cluster web contents. 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 one parameter hierarchical self-organizing model is proposed. This model has been evaluated by using the'BankSearch' benchmark dataset. Experimental results show the good performance of this approach.