A Java Technology Based Distributed Software Architecture for Web Usage Mining
Juan M. Hernansaez (University of Murcia, Spain), Juan A. Botia (University of Murcia, Spain) and Antonio F.G. Skarmeta (University of Murcia, Spain)
Copyright: © 2008
In this chapter we focus on the three approaches that seem to be the most successful ones in the Web usage mining area: clustering, association rules and sequential patterns. We will discuss some techniques from each one of these approaches, and then we will show the benefits of using METALA (a META-Learning Architecture) as an integrating tool not only for the discussed Web usage mining techniques, but also for inductive learning algorithms. As we will show, this architecture can also be used to generate new theories and models that can be useful to provide new generic applications for several supervised and non-supervised learning paradigms. As a particular example of a Web usage mining application, we will report our work for a medium-sized commercial company, and we will discuss some interesting properties and conclusions that we have obtained from our reporting.