Sebastian Lühr, Geoff West and Svetha Venkatesh (2005) An Extended Frequent Pattern Tree for Intertransaction Association Rule Mining. Technical Report TR-2005/1, Department of Computing, Curtin University of Technology

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Abstract: We propose the Extended Frequent Pattern Tree (EFP-Tree) 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.