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Emergent intertransaction association rules for abnormality detection in intelligent environments

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Open access
Authors
Luhr, Sebastian
Venkatesh, Svetha
West, Geoffrey
Date
2005
Type
Conference Paper

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Luhr, Sebastian and Venkatesh, Svetha and West, Geoffrey. 2005. Emergent intertransaction association rules for abnormality detection in intelligent environments, in Palaniswami, M. (ed), 2nd International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2005), Dec 5 2005, pp. 334-347. Melbourne, Australia: IEEE Computer Society.
Source Title
2nd International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP2005)
Source Conference
2nd International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP2005)
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School of Electrical Engineering and Computing
Department of Computing
Faculty of Science and Engineering
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Abstract

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.