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dc.contributor.convenorM.H. Hamza
dc.contributor.authorStantic, Bela
dc.contributor.authorChang, Mei-Lin
dc.contributor.editorM.H. Hamza
dc.date.accessioned2017-05-03T11:26:35Z
dc.date.available2017-05-03T11:26:35Z
dc.date.issued2010
dc.date.modified2011-10-12T06:47:36Z
dc.identifier.refurihttp://www.iasted.org/conferences/pastinfo-674.html
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.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent279572 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherACTA Press
dc.publisher.placeUSA
dc.publisher.urihttp://www.iasted.org/conferences/pastinfo-674.html
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofconferencenameTenth IASTED International Conference on Artificial Intelligence and Applications (AIA 2010)
dc.relation.ispartofconferencetitleTenth IASTED International Conference on Artificial Intelligence in Applications
dc.relation.ispartofdatefrom2010-02-15
dc.relation.ispartofdateto2010-02-17
dc.relation.ispartoflocationInnsbruck, Austria
dc.rights.retentionY
dc.subject.fieldofresearchData engineering and data science
dc.subject.fieldofresearchcode460501
dc.titleEfficient Data Mining Method to Localise Errors in RFID Data
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 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.
gro.date.issued2010
gro.hasfulltextFull Text
gro.griffith.authorStantic, Bela


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    Contains papers delivered by Griffith authors at national and international conferences.

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