Abstract
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.
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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 Spectrum Sensing and borderlands Security Key Laboratory of Universities in Yunnan (C6165903), and the Program for Young and Middle-aged Skeleton Teachers of Yunnan University.
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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: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10178. Springer, Cham. https://doi.org/10.1007/978-3-319-55699-4_28
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DOI: https://doi.org/10.1007/978-3-319-55699-4_28
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