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Erschienen in:
2018 | OriginalPaper | Buchkapitel
A Parallel Spatial Co-location Pattern Mining Approach Based on Ordered Clique Growth
verfasst von : Peizhong Yang, Lizhen Wang, Xiaoxuan Wang
Erschienen in: Database Systems for Advanced Applications
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Abstract
Co-location patterns or subsets of spatial features, whose instances are frequently located together, are particularly valuable for discovering spatial dependencies. Although lots of spatial co-location pattern mining approaches have been proposed, the computational cost is still expensive. In this paper, we propose an iterative mining framework based on MapReduce to mine co-location patterns efficiently from massive spatial data. Our approach searches for co-location patterns in parallel through expanding ordered cliques and there is no candidate set generated. A large number of experimental results on synthetic and real-world datasets show that the proposed method is efficient and scalable for massive spatial data, and is faster than other parallel methods.