A Bayesian neural network model for dynamic web document clustering

There has been lots of research to improve the precision of IR system. These research have been studied on the document ranking, user profiles, relevance feedback and the information processing that includes document classification, clustering, routing and filtering. This paper proposes and incarnates method of neural approach about the information processing which makes users can search documents effectively and of the document clustering. In this paper the system calculates entropy between the query, the profile and the each of the web documents each other; and clusters documents using the calculated entropy as the value of the clustering variable through SOM. As the Bayesian Neural Network model has high classification accuracy with a rapid learning speed and clustering, it is possible that dynamic document clustering as it was combined with Bayesian probability model used in real-time document classification. We used KTSET which is a test collection to evaluate Korean IR system for the experiment.