Traditional spatial co-location pattern mining attempts to find the subsets of spatial features whose instances are frequently located together in some regions. Most previous studies take the prevalence of co-locations as the interestingness measure. However, it is more meaningful to take the utility value of each instance into account in spatial co-location pattern mining in some cases. In this paper, we present a new interestingness measure for mining high utility co-location patterns from spatial data sets with instance-specific utilities. In the new interestingness measure, we take the intra-utility and inter-utility into consideration to capture the global influence of each feature in co-locations. We present a basic algorithm for mining high utility co-locations. In order to reduce high computational cost, some pruning strategies are given to improve the efficiency. The experiments on synthetic and real-world data sets show that the proposed method is effective and the pruning strategies are efficient.
CITATION STYLE
Wang, L., Jiang, W., Chen, H., & Fang, Y. (2017). Efficiently mining high utility co-location patterns from spatial data sets with instance-specific utilities. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10178 LNCS, pp. 458–474). Springer Verlag. https://doi.org/10.1007/978-3-319-55699-4_28
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