Pruning statistically insignificant association rules in the presence of high-confidence rules in web usage data

Abstract

Automatic discovery of web usage association rules is commonly used to extract the knowledge about web site visitors' interests. Its drawback is the generation of too many not truly interesting rules that have high statistical interestingness measures. We propose a method to prune rules that are statistically insignificant with respect to more general rules. Such rules may exist in the presence of high-confidence rules, which is often the case in web usage data. The method effectiveness is validated on two real-life web usage data sets.