Emergent intertransaction association rules for abnormality detection in intelligent environments
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This paper is concerned with identifying anomalous behaviour of people in smart environments. We propose the use of emergent transaction mining and the use of the extended frequent pattern tree as a basis. Our experiments on two data sets demonstrate that emergent intertransaction associations are able to detect abnormality present in real world data and that both short and long term behavioural changes can be discovered. The use of intertransaction associations is shown to be advantageous in the detection of temporal associationanomalies otherwise not readily detectable by traditional "market basket" intratransaction mining.
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