Web usage association rule mining system

Web usage log files generated on web servers contain huge amount of information that can be used for discovering web usage association rules, which can potentially give useful knowledge to the web usage data analysts. Association rule over-generation is a common problem in association rule mining that is further aggravated in web usage log mining due to the interconnectedness of web pages through the website link structure. We implemented a system for the discovery of association rules in web log usage data as an ob-ject-oriented application and used it to experiment on a real life web usage log data set. We proposed to alleviate the problem of web usage association rule over-generation by pruning the rules that contain directly linked pages out of the rule set. Our experiments showed that inter-estingness 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 master. We compared confidence and lift interestingness measures and found that lift outperformed con-fidence, but only after the minimum confidence threshold was taken into account.