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Predicting the Next Location Change and Time of Change for Mobile Phone Users
Date
2014-11-04
Author
Ozer, Mert
Keles, Ilkcan
Toroslu, İsmail Hakkı
Karagöz, Pınar
Ergut, Salih
Metadata
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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Predicting the next location of people from their mobile phone logs has become an active research area. Due to two main reasons this problem is very challenging: the log data is very large and there are variety of granularity levels for specifying the spatial and the temporal attributes. In this work, we focus on predicting the next location change of the user and when this change occurs. Our method has two steps, namely clustering the spatial data into larger regions and grouping temporal data into time intervals to get higher granularity levels, and then, applying sequential pattern mining technique to extract frequent movement patterns to predict the change of the region of the user and its time frame. We have validated our results with real data obtained from one of the largest mobile phone operators in Turkey. Our results are very encouraging, and we have obtained very high accuracy results.
Subject Keywords
Sequential pattern mining
,
Location prediction
,
Mobile phone users
URI
https://hdl.handle.net/11511/33306
DOI
https://doi.org/10.1145/2675316.2675318
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Department of Computer Engineering, Conference / Seminar
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Predicting the location of people from their mobile phone logs has become an active research area. Due to two main reasons this problem is very challenging: the log data is very large and there is a variety of granularity levels both for specifying the spatial and the temporal attributes, especially with low granularity level it becomes much more complicated to define common user behaviour patterns. For the location prediction problem domain, we focused on 3 sub-problems and proposed 3 different methods for...
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M. Ozer, I. Keles, İ. H. Toroslu, P. Karagöz, and S. Ergut, “Predicting the Next Location Change and Time of Change for Mobile Phone Users,” 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/33306.