A Novel GEP-Based Multiple-Layers Association Rule Mining Algorithm

摘要

To mine popular accessed Web pages items and find out their association rule from the Web server Log database for junior users providing recommendation service. A novel GEP-based algorithm for mining multiple-layers association rules was presented. Firstly, takes generalizing technology as a way to value fitness function in GEP (Gene Expression Programming). Then, relying on the significant self-search function of GEP, the most optional species was evolved. The frequent items and association rules in the next deeper layers can be mined by using traditional support-confidence method in sub-database. The algorithm improves on the frame of traditional association rule mining and uses a new evolutionary algorithm for mining association rules. Finally, the validity and efficiency of the method are presented by the application in the paper.