Predicting the Location and Time of Mobile Phone Users by Using Sequential Pattern Mining Techniques

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Date

2016-06-01

Author

Ozer, Mert
Keles, Ilkcan
Toroslu, Hakki
Karagöz, Pınar
Davulcu, Hasan

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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In recent years, using cell phone log data to model human mobility patterns became an active research area. This problem is a challenging data mining problem due to huge size and non-uniformity of the log data, which introduces several granularity levels for the specification of temporal and spatial dimensions. This paper focuses on the prediction of the location of the next activity of the mobile phone users. There are several versions of this problem. In this work, we have concentrated on the following three problems: predicting the location and the time of the next user activity, predicting the location of the next activity of the user when the location of the user changes, and predicting both the location and the time of the activity of the user when the user's location changes. We have developed sequential pattern mining-based techniques for these three problems and validated the success of these methods with real data obtained from one of the largest mobile phone operators in Turkey. Our results are very encouraging, since we were able to obtain quite high accuracy results under small prediction sets.

Subject Keywords

Human mobility patterns, Mobile phone user, Sequence mining, Location and time prediction received

URI

https://hdl.handle.net/11511/37715

Journal

COMPUTER JOURNAL

DOI

https://doi.org/10.1093/comjnl/bxv075

Collections

Department of Computer Engineering, Article

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Citation Formats

M. Ozer, I. Keles, H. Toroslu, P. Karagöz, and H. Davulcu, “Predicting the Location and Time of Mobile Phone Users by Using Sequential Pattern Mining Techniques,” COMPUTER JOURNAL, pp. 908–922, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/37715.