Hierarchical Frequent Pattern Analysis of Web Logs for Efficient Interestingness Prediction

In this paper, we proposed an efficient approach for frequent pattern mining using web logs - web usage mining and we call this approach as HFPA. In our approach HFPA, the proposed technique is applied to mine association rules from web logs using normal Apriori algorithm, but with few adaptations for improving the interestingness of the rules produced and for applicability for web usage mining. We applied this technique and compared its performance with that of classical Apriori-mined rules. The results indicate that the proposed approach HFPA not only generates far fewer rules than Apriori-based algorithms (FPA), but also generate rules of comparable quality with respect to three objective performance measures namely, Confidence, Lift and Conviction. Association mining often produces large collections of association rules that are difficult to understand and put into action. In this paper we have proposed effective pruning techniques that were characterized by the natural web link structures. Our experiments showed that interestingness measures can successfully be used to sort the discovered association rules after the pruning method was applied. Most of the rules that ranked highly according to the interestingness measures proved to be truly valuable to a web site administrator.