Mining and Validation of Localized Frequent Web Access Patterns with Dynamic Tolerance

Mining user profiles is a crucial task for Web usage mining, and can be accomplished by mining frequent patterns. However, in the Web usage domain, sessions tend to be very sparse, and mining the right user profiles tends to be difficult. Either too few or too many profiles tend to be mined, partly because of problems in fixing support thresholds and intolerant matching. Also, in the Web usage mining domain, there is often a need for post-processing and validation of the results of mining. In this paper, we use criterion guided optimization to mine localized and error-tolerant transaction patterns, instead of using exact counting based method, and explore the effect of different post-processing options on their quality. Experiments with real Web transaction data are presented.