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  1. Journal of Ambient Intelligence and Humanized Computing
  2. Journal of Ambient Intelligence and Humanized Computing : Volume 1
  3. Journal of Ambient Intelligence and Humanized Computing : Volume 1, Issue 4, December 2010
  4. Pattern mining from movement of mobile users
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Journal of Ambient Intelligence and Humanized Computing : Volume 8
Journal of Ambient Intelligence and Humanized Computing : Volume 7
Journal of Ambient Intelligence and Humanized Computing : Volume 6
Journal of Ambient Intelligence and Humanized Computing : Volume 5
Journal of Ambient Intelligence and Humanized Computing : Volume 4
Journal of Ambient Intelligence and Humanized Computing : Volume 3
Journal of Ambient Intelligence and Humanized Computing : Volume 2
Journal of Ambient Intelligence and Humanized Computing : Volume 1
Journal of Ambient Intelligence and Humanized Computing : Volume 1, Issue 4, December 2010
Special issue on emerging trends in ubiquitous computing systems
Energy efficient clustering protocol to enhance lifetime of wireless sensor network
Transmission control methods with multihopped environments in cognitive wireless networks
A route construction based on measured characteristics of radio propagation in wireless sensor networks
Evaluation of genetic algorithms for mesh router nodes placement in wireless mesh networks
Performance analysis of multi-hop ad-hoc network using multi-flow traffic for indoor scenarios
Pattern mining from movement of mobile users
Adaptive FEC algorithm based on prediction of video quality and bandwidth utilization ratio
Journal of Ambient Intelligence and Humanized Computing : Volume 1, Issue 3, September 2010
Journal of Ambient Intelligence and Humanized Computing : Volume 1, Issue 2, June 2010
Journal of Ambient Intelligence and Humanized Computing : Volume 1, Issue 1, March 2010

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Pattern mining from movement of mobile users

Content Provider Springer Nature Link
Author Iwan, Lukman Hakim Safar, Maytham
Copyright Year 2010
Abstract With the rapid progress of mobile technology, an increasing number of mobile devices are able to be tracked. In the field of cellular technology, we can track the location of a mobile phone user by locating the cell connected with his/her mobile phone. When a mobile user moves from one location to another, we can track his/her mobile device connecting from one cell to another cell. This work focuses on mining patterns from mobile user movement data. Two new algorithms are proposed, namely: location link and user link pattern mining algorithms. Both proposed algorithms are able to mine a pattern from another pattern instead of mining a pattern directly from a data source. Therefore, the pattern will be more concise and the processing time of both algorithms will be faster. Both algorithms have been evaluated from two perspectives: pattern mining generation and time processing. The experiment results from the processing time aspect indicate that larger datasets and lower values of user-defined thresholds lead to a longer processing time for both algorithms. Furthermore, the processing time of both proposed algorithms are dominated by the preparation process.
Starting Page 295
Ending Page 308
Page Count 14
File Format PDF
ISSN 18685137
Journal Journal of Ambient Intelligence and Humanized Computing
Volume Number 1
Issue Number 4
e-ISSN 18685145
Language English
Publisher Springer-Verlag
Publisher Date 2010-07-01
Publisher Place Berlin, Heidelberg
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword User Interfaces and Human Computer Interaction Robotics and Automation Artificial Intelligence (incl. Robotics) Computational Intelligence
Content Type Text
Resource Type Article
Subject Computer Science
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