Paper Info
Title
SCPM-CR: A Novel Method for Spatial Co-location Pattern Mining with Coupling Relation
Abstract
Spatial co-location pattern mining (SCPM) aims to discover subsets of spatial features frequently located in nearby geographic space. Previous studies of SCPM only concern the inter-features association of a pattern, but neglect the interesting intra-feature behavior. In this paper, we propose spatial co-location pattern mining with coupling relation consideration (SCPM-CR) to capture complex relations in a colocation. Specifically, InterPCI is proposed to capture the interfeatures coupling in a pattern, and IntraCAI is designed to capture the congregating behavior of intra-feature objects. Based on the anti-monotone property of InterPCI, a general framework which searches for patterns in a level-wise manner is suggested. To tackle the participating object search problem, a candidate-and-search algorithm with a heuristic backtracking search is proposed, namely CS-HBS. Extensive experiments are conducted to demonstrate the superiority of SCPM-CR, and also to validate the efficiency and scalability of CS-HBS.
Year
DOI
Venue
2022
10.1109/ICDE53745.2022.00128
2022 IEEE 38TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2022)
DocType
ISSN
Citations 
Conference
1084-4627
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Peizhong Yang100.34
Lizhen Wang215326.16
Xiaoxuan Wang300.34
Lihua Zhou400.34