Web Personalization and Mining Using Robust Fuzzy Clustering Methods

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This is an inter-institutional collaborative project carried out by Anupam Joshi at the University of Maryland, Baltimore County and Raghu Krishnapuram at the Colorado School of Mines. The goal of this research is to develop scalable robust techniques to model noisy data sets containing an unknown number of overlapping categories, and apply them to create a software tool for web personalization and mining. Personalization has two components: (1) tailoring the content delivered to the user from a web site; and (2) exploring the available web pages and categorizing them. The approach consists of developing new practical clustering algorithms by combining fuzzy methods, robust statistics, with Monte Carlo/bootstrapping techniques to model an unknown number of overlapping sets in the presence outliers. Since many web objects such as URLs, IP addresses, and web pages cannot be represented by numerical features, new techniques to handle web objects with linguistic and textual features, as well as to categorize or cluster them by using suitable similarity measures between such objects, are being explored. A software tool for web personalization and mining, which incorporates the algorithms developed into a software architecture, is being created and validated. The results of this project will generate new theoretical results and efficient algorithms for simultaneously estimating the parameters of an unknown number of overlapping categories from noisy data sets, as well as a web personalization and mining tool that will be made available on the web. Thus, this project is expected to have a significant impact on the way documents are searched for and delivered. It will directly influence the usefulness and spread of the Internet and WWW, and in general, will contribute to the digital library technology. http://www.cecs.missouri.edu/~joshi/web-mine/