Multiple-layers association rule mining algorithm based on GEP and it's application
To mine popular accessed web pages items and find out their association rule from the web server log database in WUM (web usage mining).A novel GEP-based algorithm for mining multiple-layers association rules is presented.Firstly,generalizing technology is taken 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 is 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.Finally,the validity and efficiency of the presented method is demonstrated by the application in big database.
GEPmultiple-layers association ruleweb usage mininggeneralizingdata miningabstract frequency items
NETL
NSTL
万方数据
维普
