Paper Info
Title
A MapReduce approach for spatial co-location pattern mining via ordered-clique-growth
Abstract
Spatial co-location pattern is a subset of spatial features whose instances are frequently located together in geography. Mining co-location patterns are particularly valuable for discovering spatial dependencies. Traditional co-location pattern mining algorithms are computationally expensive with rapidly increasing of data volume. In this paper, we explore a novel iterative framework based on parallel ordered-clique-growth for co-location pattern mining. The ordered clique extension can re-use previously processed information and be executed in parallel, and hence speed up the identification of co-location instances. Based on the iterative framework, a MapReduce algorithm is designed to search for prevalent co-location patterns in a level-wise manner, namely PCPM_OC. To narrow the search space of ordered cliques, two pruning techniques are suggested for filtering invalid clique instances as much as possible. The completeness and correctness of PCPM_OC are proven and we also discuss its complexity in this paper. Moreover, we compare PCPM_OC with two advanced MapReduce based co-location pattern mining algorithms on multiple perspectives. At last, substantial experiments are conducted on synthetic and real-world spatial datasets to study the performance of PCPM_OC. Experimental results demonstrate that PCPM_OC has a significant improvement in efficiency and shows better scalability on massive spatial data.
Year
DOI
Venue
2020
10.1007/s10619-019-07278-7
Distributed and Parallel Databases
Keywords
DocType
Volume
Spatial data mining, Co-location pattern, Parallel algorithm, MapReduce, Ordered-clique-growth
Journal
38
Issue
ISSN
Citations 
2
1573-7578
1
PageRank 
References 
Authors
0.35
0
3
Name
Order
Citations
PageRank
Peizhong Yang1226.85
Lizhen Wang215326.16
Xiaoxuan Wang310.35