Document clustering using hierarchical SOMART neural network
Mahmoud F. Hussin, Mohamed S. Kamel · 2004
Availability of large full-text document collections in electronic form has created a need for tools and techniques that assist users in organizing these collections. Document clustering is one of the popular methods used for this purpose. In this paper, we propose the neural network based document clustering method by using a hierarchically organized network built up from independent Self-Organizing Map (SOM) and Adaptive Resonance Theory (ART) neural networks. We present clustering results using the REUTERS corpus and show an improvement in clustering performance using both entropy and F-measure as evaluation measures.