A parallel spatial co-location pattern mining approach based on ordered clique growth

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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.

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Yang, P., Wang, L., & Wang, X. (2018). A parallel spatial co-location pattern mining approach based on ordered clique growth. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10827 LNCS, pp. 734–742). Springer Verlag. https://doi.org/10.1007/978-3-319-91452-7_47

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