Cultural advice

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

Aboriginal and Torres Strait Islander peoples are advised that ANU Library collections may include images, names, voices, and other representations of deceased persons.

Material in the collection may contain terms, language or views that reflect the period in which the item was created and may be considered inappropriate today.

Detecting Non-compliant Consumers in Spatio-Temporal Health Data: A Case Study from Medicare Australia

Simple item page
dc.contributor.authorNg, Kee Siong
dc.contributor.authorShan, Y.
dc.contributor.authorMurray, D.W.
dc.contributor.authorSutinen, A.
dc.contributor.authorScharwz, B.
dc.contributor.authorJeacocke, D.
dc.contributor.authorFarrugia, J.
dc.coverage.spatialSydney Australia
dc.date.accessioned2015-12-10T22:40:35Z
dc.date.createdDecember 13 2010
dc.date.issued2010
dc.date.updated2015-12-09T11:00:12Z
dc.description.abstractThis paper describes our experience with applying data mining techniques to the problem of fraud detection in spatio-temporal health data in Medicare Australia. A modular framework that brings together disparate data mining techniques is adopted. Several generally applicable techniques for extracting features from spatial and temporal data are also discussed. The system was evaluated with input from domain experts and was found to achieve high hit rates. We also discuss some lessons drawn from the experience.
dc.identifier.isbn9780769542560
dc.identifier.urihttp://hdl.handle.net/1885/57514
dc.publisherIEEE Computer Society
dc.relation.ispartofseriesIEEE International Conference on Data Mining (ICDM 2010)
dc.sourceIEEE International Conference on Data Mining (ICDM 2010) proceedings
dc.source.urihttp://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=7127&copyownerid=7413
dc.titleDetecting Non-compliant Consumers in Spatio-Temporal Health Data: A Case Study from Medicare Australia
dc.typeConference paper
local.contributor.affiliationNg, Kee Siong, College of Engineering and Computer Science, ANU
local.contributor.affiliationShan, Y., Medicare Australia
local.contributor.affiliationMurray, D.W., Medicare Australia
local.contributor.affiliationSutinen, A., Medicare Australia
local.contributor.affiliationScharwz, B., Medicare Australia
local.contributor.affiliationJeacocke, D., Medicare Australia
local.contributor.affiliationFarrugia, J., Medicare Australia
local.contributor.authoruidNg, Kee Siong, u9914730
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080109 - Pattern Recognition and Data Mining
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
local.identifier.ariespublicationU3594520xPUB404
local.type.statusPublished Version

Downloads

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
01_Ng_Detecting_Non-compliant_2010.pdf
Size:
296.43 KB
Format:
Adobe Portable Document Format
Download

Collections

ANU Research Publications

DSpace software copyright © 2002-2026 LYRASIS