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Effectively Updating High Utility Co-location Patterns in Evolving Spatial Databases

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Web-Age Information Management (WAIM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9658))

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Abstract

Spatial co-location mining has been used for discovering spatial feature sets which show frequent association relationships based on the spatial neighborhood. In spatial high utility co-location mining, we should consider the utility as a measure of interests, by considering the different value of individual instance that belongs to different feature. This paper presents a problem of updating high utility co-locations on evolving spatial databases which are updated with fresh data at some areas. Updating spatial patterns is a complicated process in that fresh data increase the new neighbor relationships. The increasing of neighbors can affect the result of high utility co-location mining. This paper proposes an algorithm for efficiently updating high utility co-locations and evaluates the algorithm by experiments.

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Acknowledgment

This work was supported in part by grants (No. 61472346, No. 61262069) from the National Natural Science Foundation of China and in part by a grant (No. 2015FB149, No. 2015FB114) from the Science Foundation of Yunnan Province.

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Authors and Affiliations

  1. Department of Information Science and Engineering, Yunnan University, No. 2 Cuihu North Road, Wuhua District, Kunming City, Yunnan Province, China

    Xiaoxuan Wang, Lizhen Wang, Junli Lu & Lihua Zhou

Authors
  1. Xiaoxuan Wang
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  2. Lizhen Wang
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  3. Junli Lu
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  4. Lihua Zhou
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Corresponding author

Correspondence to Lizhen Wang .

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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Wang, X., Wang, L., Lu, J., Zhou, L. (2016). Effectively Updating High Utility Co-location Patterns in Evolving Spatial Databases. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9658. Springer, Cham. https://doi.org/10.1007/978-3-319-39937-9_6

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  • DOI: https://doi.org/10.1007/978-3-319-39937-9_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39936-2

  • Online ISBN: 978-3-319-39937-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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