Automatic Web site classification in a large repository under information filtering and retrieval techniques

Nowadays, there is a huge volume of information on the Web, which is disseminated to the users in a chaotic way. In order to be easily accessed, the information must be clustered and classified in appropriate knowledge areas. Thus, many heavily visited sites or portals try to unify the access to multiple information sources, providing by this way classification of information. This paper proposes a system, aiming to classify e-commerce sites according their Web-content. This system can be implemented for automatic knowledge segmentation in a portal or in a search engine repository. The system performance reached 96% in the first test sets, after the learning phase. However, the performance significantly increases (up to 98%) as the number of the test sets increases.