Automatic category generation for text documents by self-organizing maps

One important task for text data mining is automatic text categorization, which assigns a text document to some predefined category according to their correlations. Traditionally, these categories as well as the correlations among them are determined bp human experts. In this paper, we devised a novel approach to automatically generate categories. The self-organizing map model is used to generate two maps, namely the word cluster map and the document cluster map, in which a neuron represents a cluster of words and documents respectively. Our approach is to analyze the document cluster map to find centroids of some super-clusters. We also devised a method to select the category term from the word cluster map. The hierarchical structure of categories may be generated by recursively applying the same method. Text categorization is the natural consequence of such automatic category generation process.