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dc.contributor.convenorM.H. Hamzaen_AU
dc.contributor.authorStantic, Belaen_US
dc.contributor.authorChang, Mei-Linen_US
dc.contributor.editorM.H. Hamzaen_US
dc.date.accessioned2017-05-03T11:26:35Z
dc.date.available2017-05-03T11:26:35Z
dc.date.issued2010en_US
dc.date.modified2011-10-12T06:47:36Z
dc.identifier.refurihttp://www.iasted.org/conferences/pastinfo-674.htmlen_AU
dc.identifier.urihttp://hdl.handle.net/10072/37711
dc.description.abstractSince the emergence of Radio Frequency Identi?cation technology (RFID), the community has been promised a cost effective and ef?cient means to identify and track large number of items with relative ease. Unfortunately, due to the unreliable nature of the passive architecture, the RFID revolution has been reduced to a fraction of intended audience due to the anomalies. These anomalies are duplicate, positive and negative readings. While duplicate readings and wrong data (false positive) can be easily identi?ed and recti?ed, that is not the case for false negative or missed readings. To identify missed readings data mining methods can be used. However, due to its vast volume and complex spatio-temporal structure of RFID data, traditional data mining methods are not necessarily directly applicable. In this paper we propose method to identify possible missed RFID readings by applying association rules data mining method. In empirical study we show that our algorithm is accurate and ef?cient and also we show that it scales well with increased number of rows therefore it is applicable on vast volume on spatio-temporal RFID data.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent279572 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherACTA Pressen_US
dc.publisher.placeUSAen_US
dc.publisher.urihttp://www.iasted.org/conferences/pastinfo-674.htmlen_AU
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofconferencenameTenth IASTED International Conference on Artificial Intelligence and Applications (AIA 2010)en_US
dc.relation.ispartofconferencetitleTenth IASTED International Conference on Artificial Intelligence in Applicationsen_US
dc.relation.ispartofdatefrom2010-02-15en_US
dc.relation.ispartofdateto2010-02-17en_US
dc.relation.ispartoflocationInnsbruck, Austriaen_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchData Format not elsewhere classifieden_US
dc.subject.fieldofresearchcode080499en_US
dc.titleEfficient Data Mining Method to Localise Errors in RFID Dataen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.rights.copyrightCopyright 2010 IASTED and ACTA Press. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the conference's website for access to the definitive, published version.en_AU
gro.date.issued2010
gro.hasfulltextFull Text


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