Abstract
Spatial co-location pattern represents a subset of spatial features whose instances are frequently located together in space. Sub-prevalent co-location pattern mining discovers patterns with richer spatial relationships based on star instance model instead of clique instance model. Further, discovering spatiotemporal sub-prevalent co-location pattern is important to reveal the spatiotemporal interaction between spatial features and promote the application of patterns. However, the methods for mining spatiotemporal sub-prevalent co-location pattern measure the interestingness of patterns by the frequency of patterns in time slice set, and ignore the duration of patterns which is an important spatiotemproal information in patterns. Thus, this paper presents mining spatiotemporal sub-prevalent co-location pattern by considering the duration and the frequency of patterns. Specifically, a novel pattern, is proposed by defining the continuous sub-prevalent index. Then, an efficient algorithm is designed to mine the proposed patterns by utilizing the anti-monotonicity of continuous sub-prevalent index to prune unpromising patterns. Extensive experiments on synthetic and real datasets verify the practicability of the proposed patterns and the effectiveness of the proposed algorithm.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (62266050, 62276227), the Program for Young and Middle-aged Academic and Technical Reserve Leaders of Yunnan Province (202205AC160033), Yunnan Provincial Major Science and Technology Special Plan Projects (202202AD080003), the Open Project Program of Yunnan Key Laboratory of Intelligent Systems and Computing (ISC22Z02), Yunnan Fundamental Research Projects (202201AS070015).
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Wang, Q., Chen, H., Wang, L. (2023). Continuous Sub-prevalent Co-location Pattern Mining. In: Meng, X., et al. Spatial Data and Intelligence. SpatialDI 2023. Lecture Notes in Computer Science, vol 13887. Springer, Cham. https://doi.org/10.1007/978-3-031-32910-4_14
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