Location Prediction of Mobile Phone Users Using Apriori-Based Sequence Mining with Multiple Support Thresholds

Due to the increasing use of mobile phones and their increasing capabilities, huge amount of usage and location data can be collected. Location prediction is an important task for mobile phone operators and smart city administrations to provide better services and recommendations. In this work, we propose a sequence mining based approach for location prediction of mobile phone users. More specifically, we present a modified Apriori-based sequence mining algorithm for the next location prediction, which involves use of multiple support thresholds for different levels of pattern generation process. The proposed algorithm involves a new support definition, as well. We have analyzed the behaviour of the algorithm under the change of threshold through experimental evaluation and the experiments indicate improvement in comparison to conventional Apriori-based algorithm.

[1]  Özgür Ulusoy,et al.  A data mining approach for location prediction in mobile environments , 2005, Data Knowl. Eng..

[2]  César A. Hidalgo,et al.  Unique in the Crowd: The privacy bounds of human mobility , 2013, Scientific Reports.

[3]  Wen-Yang Lin,et al.  Mining Generalized Association Rules with Multiple Minimum Supports , 2001, DaWaK.

[4]  Yen-Liang Chen,et al.  Mining association rules with multiple minimum supports: a new mining algorithm and a support tuning mechanism , 2004, Decision Support Systems.

[5]  Wynne Hsu,et al.  Mining association rules with multiple minimum supports , 1999, KDD '99.

[6]  Elena Baralis,et al.  A lazy approach to pruning classification rules , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[7]  Ismail Hakki Toroslu,et al.  Predicting the change of location of mobile phone users , 2013, MobiGIS '13.

[8]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[9]  P. Krishna Reddy,et al.  Novel techniques to reduce search space in multiple minimum supports-based frequent pattern mining algorithms , 2011, EDBT/ICDT '11.

[10]  Murat Kantarcioglu,et al.  Mining Cyclically Repeated Patterns , 2001, DaWaK.

[11]  Shuo Wang,et al.  An improved multi-support Apriori algorithm under the fuzzy item association condition , 2011, 2011 International Conference on Electronics, Communications and Control (ICECC).

[12]  Wang-Chien Lee,et al.  Semantic trajectory mining for location prediction , 2011, GIS.

[13]  Jiawei Han,et al.  Discovery of Multiple-Level Association Rules from Large Databases , 1995, VLDB.