Discovery of Interesting Association Rules Based on Web Usage Mining

Abstract

Mining of association rules is an important research topic in web usage mining. The purpose of this paper is to research how to dig interesting association rules effectively from the Web logs after been preprocessed. Firstly, using the FP-growth algorithm for processing the web log records, obtaining a set of frequent access patterns, then using the combination of browse interestingness and site topology interestingness of association rules for web mining, discovering a new pattern to provide valuable data for the site construction.