Knowledge Discovery & Web Mining Lab, University of Louisville

NSF CAREER: New Clustering Algorithms Based on Robust Estimation and Genetic Niches

with Applications to Web Usage Mining


  Goals, Objectives and Targeted Activities

  Selected Developments


Area Background

Data sets
 Outreach Activities

This project is supported by National Science Foundation under grant NSF IIS-0533317


 Area Background

As a valuable unsupervised learning tool, clustering is crucial to many applications. Genetic Algorithms have been used with success as global searchers in difficult problems, particularly in the optimization of non-differentiable functions. Recently, several Genetic Niching (GN) techniques have emerged to tackle multimodal function optimization. Current genetic clustering techniques are not robust in the presence of noise; assume a known number of clusters; and suffer from a search space size that explodes exponentially with the number of clusters. Web Personalization tailors a user’s interaction with the Web information space based on information gathered about them. Declarative user information such as manually entered profiles continue to raise privacy concerns and are neither scalable nor flexible in the face of very active dynamic Web sites and changing user trends and interests. One way to deal with this problem is through an automated Web personalization system. Such a system can be based on Web usage mining to discover Web usage profiles, followed by a recommendation system that can respond to the users’ individual interests.

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