Efficient Data Mining Method to Localise Errors in RFID Data

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Title Efficient Data Mining Method to Localise Errors in RFID Data
Author Stantic, Bela; Chang, Mei-Lin
Publication Title Tenth IASTED International Conference on Artificial Intelligence in Applications
Editor M.H. Hamza
Year Published 2010
Place of publication USA
Publisher ACTA Press
Abstract Since the emergence of Radio Frequency Identification technology (RFID), the community has been promised a cost effective and efficient 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 identified and rectified, 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 efficient 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.
Peer Reviewed Yes
Published Yes
Publisher URI http://www.iasted.org/conferences/pastinfo-674.html
Copyright Statement 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.
ISBN 978-0-88986-818-2
Conference name Tenth IASTED International Conference on Artificial Intelligence and Applications (AIA 2010)
Location Innsbruck, Austria
Date From 2010-02-15
Date To 2010-02-17
URI http://hdl.handle.net/10072/37711
Date Accessioned 2011-01-31
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Data Format
Publication Type Conference Publications (Full Written Paper - Refereed)
Publication Type Code e1

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