An extended frequent pattern tree for intertransaction association rule mining

We propose the Extended Frequent Pattern Tree (EFPTree) to address the problem of intertransaction association rule mining where the frequent occurrence of a large number of items results in a combinatorial explosion that limits the practical application of the existing Apriori inspired mining algorithms in a smart home environment. The EFP-Tree mining algorithm avoids candidate generation by employing a divide and conquer approach that recursively finds the set of frequent intertransaction association rules. Empirical results comparing the computational performance of the EFP-Tree with the First Intra Then Inter (FITI) algorithm on real world data from a smart home are presented. Experimental results show significant computational improvement of the EFP-Tree over FITI when a large number of rules is present in the data.