Web Usage Mining Using Rough Sets
Natheer Khasawneh, Chien-Chung Chan · 2005
This paper studies the use of a rough set based learning program for predicting Web usage. In our approach, Web usage patterns are represented as rules generated by the inductive learning program, BLEM2. Inputs to BLEM2 are clusters generated by a hierarchical clustering algorithm applied to preprocessed Web log records. Empirical results show that the prediction accuracy of rules induced by the learning program is better than a centroid based method. In addition, the use of a learning program can generate shorter cluster descriptions.