One possible approach is to personalize the web space -- create a system which responds to user queries by potentially aggregating information from several sources in a manner which is dependent on who the user is.

Existing commercial systems seek to do some minimal personalization based on declarative information directly provided by the user, such as their zip code, or keywords describing their interests, or specific URLs, or even particular pieces of information they are interested in (e.g. price for a particular stock). Our research aims at creating systems that (semi) automatically tailor the content delivered to the user from a web site. We do so by mining the web -- both the contents, as well as the users' interaction.

Web Mining and Personalization requires modeling of an unknown number of overlapping sets in the presence of significant noise and outliers, (i. e., bad exemplars). Moreover, the data sets in Web Mining are extremely large. The aim of our reserach is to develop scalable robust fuzzy techniques to model noisy data sets containing an unknown number of overlapping categories. Specifically, in this work we are :

1. Developing new scalable robust fuzzy clustering techniques for modeling data
2. Exploring new techniques to handle linguistic and textual features
3. Validating our techniques by creating prototype web mining and personalization systems