SCPM-CR: A Novel Method for Spatial Co-Location Pattern Mining With Coupling Relation Consideration

• Published in: IEEE transactions on knowledge and data engineering Vol. 34; no. 12; pp. 5979 - 5992
• Main Authors: Yang, Peizhong, Wang, Lizhen, Wang, Xiaoxuan, Zhou, Lihua
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Atmospheric measurements; co-location pattern; Coupling; coupling relation; Couplings; Data mining; Feature extraction; heuristic algorithm; Indexes; Particle measurements; Pattern analysis; Search algorithms; Spatial data mining; Spatial databases
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2021.3060119

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.