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A MapReduce approach for spatial co-location pattern mining via ordered-clique-growth

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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.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (61472346, 61662086, 61762090), and the Project of Innovative Research Team of Yunnan Province (2018HC019).

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  1. School of Information Science and Engineering, Yunnan University, Kunming, 650091, China

    Peizhong Yang, Lizhen Wang & Xiaoxuan Wang

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  1. Peizhong Yang
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  2. Lizhen Wang
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  3. Xiaoxuan Wang
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Correspondence to Lizhen Wang.

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Yang, P., Wang, L. & Wang, X. A MapReduce approach for spatial co-location pattern mining via ordered-clique-growth. Distrib Parallel Databases 38, 531–560 (2020). https://doi.org/10.1007/s10619-019-07278-7

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