Exploring Regression for Mining User Moving Patterns in a Mobile Computing System

In this paper, by exploiting the log of call detail records, we present a solution procedure of mining user moving patterns in a mobile computing system. Specifically, we propose algorithm LS to accurately determine similar moving sequences from the log of call detail records so as to obtain moving behaviors of users. By exploring the feature of spatial-temporal locality, we develop algorithm TC to group call detail records into clusters. In light of the concept of regression, we devise algorithm MF to derive moving functions of moving behaviors. Performance of the proposed solution procedure is analyzed and sensitivity analysis on several design parameters is conducted. It is shown by our simulation results that user moving patterns obtained by our solution procedure are of very high quality and in fact very close to real user moving behaviors.

[1]  Jorng-Tzong Horng,et al.  Personal paging area design based on mobile's moving behaviors , 2001, Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213).

[2]  Jiawei Han,et al.  Efficient mining of partial periodic patterns in time series database , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[3]  M. Kendall.  Probability and Statistical Inference , 1956, Nature.

[4]  Ming-Syan Chen,et al.  Developing Data Allocation Schemes by Incremental Mining of User Moving Patterns in a Mobile Computing System , 2003, IEEE Trans. Knowl. Data Eng..

[5]  Yi-Bing Lin,et al.  Modeling techniques for large-scale PCS networks , 1997, IEEE Commun. Mag..

[6]  Jianliang Xu,et al.  Data Management in Location-Dependent Information Services , 2002, IEEE Pervasive Comput..

[7]  P. J. Green,et al.  Probability and Statistical Inference , 1978 .