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Data & Knowledge Engineering
Volume 54, Issue 2, August 2005, Pages 121-146
 
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doi:10.1016/j.datak.2004.09.004    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2004 Elsevier B.V. All rights reserved.

A data mining approach for location prediction in mobile environmentsstar, open

Gökhan Yavaşa, E-mail The Corresponding Author, Dimitrios Katsarosb, E-mail The Corresponding Author, Özgür Ulusoya, Corresponding Author Contact Information, E-mail The Corresponding Author and Yannis Manolopoulosb, E-mail The Corresponding Author

aDepartment of Computer Engineering, Bilkent University, Bilkent, Ankara 06533, Turkey bDepartment of Informatics, Aristotle University, Thessaloniki, Greece

Received 3 May 2004; 
accepted 30 September 2004. 
Available online 30 October 2004.

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Abstract

Mobility prediction is one of the most essential issues that need to be explored for mobility management in mobile computing systems. In this paper, we propose a new algorithm for predicting the next inter-cell movement of a mobile user in a Personal Communication Systems network. In the first phase of our three-phase algorithm, user mobility patterns are mined from the history of mobile user trajectories. In the second phase, mobility rules are extracted from these patterns, and in the last phase, mobility predictions are accomplished by using these rules. The performance of the proposed algorithm is evaluated through simulation as compared to two other prediction methods. The performance results obtained in terms of Precision and Recall indicate that our method can make more accurate predictions than the other methods.

Keywords: Location prediction; Data mining; Mobile computing; Mobility patterns; Mobility prediction

Article Outline

1. Introduction
2. Background
2.1. Problem definition
2.2. Related work
3. Mobility prediction based on mobility rules
3.1. Mining user mobility patterns from graph traversals
3.2. Generation of mobility rules
3.3. Mobility prediction
4. Experimental results
4.1. Simulation design
4.2. Algorithms used for comparison
4.3. Impact of maximum number of predictions
4.4. Impact of minimum support value
4.5. Impact of minimum confidence value
4.6. Impact of corruption factor
4.7. Impact of outlier percentage
5. Conclusion
References
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