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Please use this identifier to cite or link to this item: http://hdl.handle.net/2328/9599

Title: Detecting anomalous longitudinal associations through higher order mining
Authors: Liang, Ping
Roddick, John Francis
Issue Date: 2007
Publisher: Australian Computer Society Inc
Citation: Liang, P. & Roddick, J.F., 2007. Detecting anomalous longitudinal associations through higher order mining. Integrating Artificial Intelligence and Data Mining: Proceedings of the 2nd International Workshop on Integrating Artificial Intelligence and Data Mining (AIDM 2007), 84, 19-27.
Abstract: The detection of unusual or anomalous data is an important function in automated data analysis or data mining. However, the diversity of anomaly detection algorithms shows that it is often difficult to determine which algorithms might detect anomalies given any random dataset. In this paper we provide a partial solution to this problem by elevating the search for anomalous data in transaction-oriented datasets to an inspection of the rules that can be produced by higher order longitudinal/spatio-temporal association rule mining. In this way we are able to apply algorithms that may provide a view of anomalies that is arguably closer to that sought by information analysts.
URI: http://crpit.com/abstracts/CRPITV84Liang.html
http://hdl.handle.net/2328/9599
ISBN: 9781920682651
Appears in Collections:0801 - Artificial Intelligence and Image Processing

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