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Journal of Zhejiang University-SCIENCE A

, Volume 10, Issue 2, pp 221–231 | Cite as

Personal continuous route pattern mining

Article

Abstract

In the daily life, people often repeat regular routes in certain periods. In this paper, a mining system is developed to find the continuous route patterns of personal past trips. In order to count the diversity of personal moving status, the mining system employs the adaptive GPS data recording and five data filters to guarantee the clean trips data. The mining system uses a client/server architecture to protect personal privacy and to reduce the computational load. The server conducts the main mining procedure but with insufficient information to recover real personal routes. In order to improve the scalability of sequential pattern mining, a novel pattern mining algorithm, continuous route pattern mining (CRPM), is proposed. This algorithm can tolerate the different disturbances in real routes and extract the frequent patterns. Experimental results based on nine persons’ trips show that CRPM can extract more than two times longer route patterns than the traditional route pattern mining algorithms.

Key words

Data mining Route pattern GPS Mobile phone 

CLC number

TP39 

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Copyright information

© Zhejiang University and Springer-Verlag GmbH 2009

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyZhejiang UniversityHangzhouChina

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