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.
CITATION STYLE
Wang, X., Wang, L., Lu, J., & Zhou, L. (2016). Effectively updating high utility co-location patterns in evolving spatial databases. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9658, pp. 67–81). Springer Verlag. https://doi.org/10.1007/978-3-319-39937-9_6
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