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dc.contributor.authorLiang, Ping
dc.contributor.authorRoddick, John Francis
dc.date.accessioned2010-07-27T05:57:31Z
dc.date.available2010-07-27T05:57:31Z
dc.date.issued2007en_US
dc.identifier.citationLiang, 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.en
dc.identifier.isbn978-1920682651
dc.identifier.urihttp://hdl.handle.net/2328/9599
dc.identifier.urihttp://crpit.com/abstracts/CRPITV84Liang.html
dc.description.abstractThe 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.en
dc.publisherAustralian Computer Societyen
dc.relation.ispartofseriesSecond International Workshop on Integrating AI and Data Mining (AIDM 2007)en
dc.titleDetecting anomalous longitudinal associations through higher order miningen
dc.typeConference paperen
dc.identifier.rmid2006005902
dc.description.noteSydney, NSWen
dc.subject.forgroup0801 Artificial Intelligence and Image Processingen
dc.subject.forgroup0804 Data Formaten
dc.subject.forgroup0806 Information Systemsen
dc.rights.licenseIn Copyright
local.contributor.authorOrcidLookupRoddick, John Francis: https://orcid.org/0000-0001-7024-0796en_US


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