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.

Sebastian Lühr, Svetha Venkatesh, Geoff West and Hung H. Bui (2004) Duration Abnormality Detection in Sequences of Human Activity. Technical Report TR-2004/02, Department of Computing, Curtin University of Technology

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Abstract: Activity duration is an essential element in the accurate modelling of human behaviour. The application of a standard hidden Markov Model (HMM) for the detection of abnormality in sequences of human activity can create a situation in which highly unusual duration less than or greater than the duration normally observed can fail to be detected. In this paper, we show how the application of the explicit state duration HMM can alleviate this problem, enabling us to distinguish between sequences of activity in which the order of observations is identical but the duration of activities is different and to identify the presence of abnormal activity duration. Experimental results highlight the improvement over the standard HMM for both abnormality detection and classification in certain anomalous situations.