Abstract
The rapid increase in web sites has created large volumes of data in web environment, which generates a problem of how to extract and gain useful knowledge from such data. Extracting such knowledge helps to discover, understand and predict user behaviors based on his interaction with the website. This paper introduces two neural network based systems, one for web visitor recognition according to their web logs pattern, and the other for web visitor classification according to the visited pages. This will introduce rapid services and save user time with web through a database connected to these neural networks Such system can be used to improve efficiency and effectiveness in searching for information on the web. Complete architecture of the networks is given based on supervised and unsupervised learning paradigms. Experiments have been carried out in order to validate this approach.
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