An extended frequent pattern tree for intertransaction association rule mining
Sebastian Lühr, GEOFF A. W. WEST, Svetha Venkatesh · Deakin Research Online (Deakin University) · 2005
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 in spired 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.