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Valuable Group Trajectory Pattern Mining Directed by Adaptable Value Measuring Model

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Web-Age Information Management (WAIM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9659))

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Abstract

Group trajectory pattern mining for large amounts of mobile customers is a practical task in broad applications. Usually the pattern mining result is a set of mined patterns with their support. However, most of them are not valuable for users and it is difficult for users to go through all mined patterns to find valuable ones. In this paper, instead of just mining group trajectory patterns, we investigate how to mine the top valuable patterns for users, which has not been well solved yet given the following two challenges. The first is how to estimate the value of trajectory patterns according to users’ requirements. Second, there are redundant information in the mined results because many mined patterns share common sub-patterns. To address these challenges, we define an adaptable value measuring model by leveraging multi-factors correlation in users’ requirements, which is used to estimate the value of trajectory patterns. In order to reduce the redundant sub-patterns, we propose a new group trajectory pattern mining approach directed by the adaptable value measuring model. In addition, we extend and implement the algorithm as a parallel algorithm in cloud computing platform to deal with massive data. Experiments on real massive mobile data show the effectiveness and efficiency of the proposed approach.

This research is supported by the Natural Science Foundation of China (Grant No. 61572043, 61300003).

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Authors and Affiliations

  1. School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China

    Xinyu Huang, Tengjiao Wang & Wei Chen

  2. Key Laboratory of High Confidence Software Technologies, Ministry of Education, Beijing, 100871, China

    Xinyu Huang, Tengjiao Wang & Wei Chen

  3. School of Information Science and Technology, University of International Relations, Beijing, China

    Shun Li

Authors
  1. Xinyu Huang
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  2. Tengjiao Wang
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  3. Shun Li
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  4. Wei Chen
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Corresponding author

Correspondence to Shun Li .

Editor information

Editors and Affiliations

  1. Peking University , Beijing, China

    Bin Cui

  2. The George Washington University , Washington, D.C., USA

    Nan Zhang

  3. Hong Kong Baptist University, Kowloon Tong, Hong Kong, China

    Jianliang Xu

  4. University of Texas Rio Grande Valley, Edinburg, Texas, USA

    Xiang Lian

  5. Jiangxi University of Finance and Economics, Nanchang, Jiangxi, China

    Dexi Liu

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© 2016 Springer International Publishing Switzerland

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Huang, X., Wang, T., Li, S., Chen, W. (2016). Valuable Group Trajectory Pattern Mining Directed by Adaptable Value Measuring Model. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9659. Springer, Cham. https://doi.org/10.1007/978-3-319-39958-4_30

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  • DOI: https://doi.org/10.1007/978-3-319-39958-4_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39957-7

  • Online ISBN: 978-3-319-39958-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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