A comparison of SOM based document categorization systems

This paper describes the development and evaluation of two unsupervised learning mechanisms for solving the automatic document categorization problem. Both mechanisms are based on a hierarchical structure of self-organizing feature maps. Specifically, one architecture is based on the vector space model whereas the other one is based on a code-books model. Results show that the latter architecture performs better than the first one which is based on the quality of the returned clusters.