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Received: 2008-03-17

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Crosschecked: 2008-12-25

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Journal of Zhejiang University SCIENCE A 2009 Vol.10 No.2 P.221~231

http://doi.org/10.1631/jzus.A0820193


Personal continuous route pattern mining


Author(s):  Qian YE, Ling CHEN, Gen-cai CHEN

Affiliation(s):  School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   yeqian.zju@gmail.com, lingchen@zju.edu.cn, chengc@zju.edu.cn

Key Words:  Data mining, Route pattern, GPS, Mobile phone


Qian YE, Ling CHEN, Gen-cai CHEN. Personal continuous route pattern mining[J]. Journal of Zhejiang University Science A, 2009, 10(2): 221~231.

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author="Qian YE, Ling CHEN, Gen-cai CHEN",
journal="Journal of Zhejiang University Science A",
volume="10",
number="2",
pages="221~231",
year="2009",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A0820193"
}

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%A Qian YE
%A Ling CHEN
%A Gen-cai CHEN
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%D 2009
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0820193

TY - JOUR
T1 - Personal continuous route pattern mining
A1 - Qian YE
A1 - Ling CHEN
A1 - Gen-cai CHEN
J0 - Journal of Zhejiang University Science A
VL - 10
IS - 2
SP - 221
EP - 231
%@ 1673-565X
Y1 - 2009
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A0820193


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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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