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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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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