A Formal Concept Analysis Approach for Web Usage Mining

Web usage mining aims to discover interesting user access patterns from web logs. Formal Concept Analysis (FCA) is an effective data analysis technique based on ordered lattice theory. In this paper, we propose a novel FCA approach for web usage mining. In our approach, the FCA technique is applied to mine association rules from Web Usage Lattice constructed from web logs. The discovered knowledge (association rules) can then be used for practical web applications such as web recommendation and personalization. We apply the FCA-mined association rules to web recommendation and compare its performance with that of classical Apriori-mined rules. The results indicate that the proposed FCA approach not only generates far fewer rules than Apriori-based algorithms, the generated rules are also of comparable quality with respect to three objective performance measures.