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Mining Co-location Patterns with Dominant Features

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Web Information Systems Engineering – WISE 2017 (WISE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10569))

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Abstract

The spatial co-location pattern mining discovers the subsets of spatial features which are located together frequently in geography. Most of the studies in this field use prevalence to measure a co-location pattern’s popularity, namely the frequencies of a spatial feature set participating in a spatial database. However, in some cases, users are not only interested in identifying the prevalence of a feature set, but also the features playing the dominant role in a pattern. In this paper, we focus on mining dominant-feature co-location pattern (DFCP). We firstly propose a new measure, namely disparity, to measure the disparity of features in a pattern. Secondly, we formulate the DFCP mining problem to determine DFCP and extract dominant features. Thirdly, an efficient algorithm is proposed for mining DFCP. Finally, we offer an experimental evaluation of the proposed algorithms on both real data sets and synthetic data sets in terms of efficiency, mining results and significance. The results show that our method can effectively discover DFCPs.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (61472346, 61662086), the Natural Science Foundation of Yunnan Province (2015FB114, 2016FA026), the Project of Innovation Team of Yunnan University.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650091, China

    Yuan Fang, Lizhen Wang, Xiaoxuan Wang & Lihua Zhou

Authors
  1. Yuan Fang
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  2. Lizhen Wang
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  3. Xiaoxuan Wang
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  4. Lihua Zhou
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Corresponding author

Correspondence to Lizhen Wang .

Editor information

Editors and Affiliations

  1. University of Sydney, Darlington, NSW, Australia

    Athman Bouguettaya

  2. Zhejiang University, Hangzhou, China

    Yunjun Gao

  3. Institute of Computing for Physics and Technology, Protvino, Russia

    Andrey Klimenko

  4. Nanyang Technological University, Singapore, Singapore

    Lu Chen

  5. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Xiangliang Zhang

  6. Institute of Computing for Physics and Technology, Protvino, Russia

    Fedor Dzerzhinskiy

  7. Shanghai Jiao Tong University, Minhang Qu, China

    Weijia Jia

  8. Institute of Computing for Physics and Technology, Protvino, Russia

    Stanislav V. Klimenko

  9. City University of Hong Kong, Kowloon, Hong Kong

    Qing Li

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Fang, Y., Wang, L., Wang, X., Zhou, L. (2017). Mining Co-location Patterns with Dominant Features. In: Bouguettaya, A., et al. Web Information Systems Engineering – WISE 2017. WISE 2017. Lecture Notes in Computer Science(), vol 10569. Springer, Cham. https://doi.org/10.1007/978-3-319-68783-4_13

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  • DOI: https://doi.org/10.1007/978-3-319-68783-4_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68782-7

  • Online ISBN: 978-3-319-68783-4

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