SCPM-CR: A Novel Method for Spatial Co-Location Pattern Mining With Coupling Relation Consideration
SCPM-CR: A Novel Method for Spatial Co-Location Pattern Mining With Coupling Relation Consideration
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DOI：
10.1109/tkde.2021.3060119
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发表时间：
2021-02
影响因子：
8.9
通讯作者：
Peizhong Yang;Lizhen Wang;Xiaoxuan Wang;Lihua Zhou
中科院分区：
文献类型：
--
作者：
Peizhong Yang;Lizhen Wang;Xiaoxuan Wang;Lihua Zhou
Spatial co-location pattern mining (SCPM) aims to discover subsets of spatial features frequently located together in proximate areas. Previous studies of SCPM solely concern the inter-features association of a pattern, but neglect the interesting intra-feature behavior. In this paper, we propose the task of spatial co-location pattern mining with coupling relation consideration (SCPM-CR) to capture complex relations embedded in a co-location. Specifically, InterPCI measure is designed to capture the inter-features coupling by considering the comprehensive interaction of objects for the features in a pattern, and luckily it possesses the anti-monotone property. Another measure, IntraCAI, is proposed to capture the congregating behavior of intra-feature objects under the restriction of a co-location. A general framework is designed for SCPM-CR task and experiments show that a large fraction of computation time is devoted to identifying the participating objects of a candidate pattern. To tackle this calculation bottleneck, a novel candidate-and-search algorithm is suggested, CS-HBS, equipped with heuristic backtracking search. Extensive experiments are conducted on real and synthetic datasets to demonstrate the superiority of SCPM-CR compared with traditional SCPM methods, and also to validate the efficiency and scalability of CS-HBS. Experimental results show that CS-HBS outperforms the baselines by several times or even orders of magnitude.
