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Efficient discovery of co-location patterns from massive spatial datasets with or without rare features

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Abstract

A co-location pattern indicates a group of spatial features whose instances are frequently located together in proximate geographic area. Spatial co-location pattern mining (SCPM) is valuable for many practical applications. Numerous previous SCPM studies emphasize the equal participation per feature. As a result, the interesting co-locations with rare features cannot be captured. In this paper, we propose a novel interest measure, i.e., the weighted participation index (WPI), to identify co-locations with or without rare features. The WPI measure possesses a conditional anti-monotone property which can be utilized to prune the search space. In addition, a fast row instance identification mechanism based on the ordered NR-tree is proposed to enhance efficiency. Subsequently, the ordered NR-tree-based algorithm is developed. To further improve efficiency and process massive spatial data, we break the ordered NR-tree into multiple independent subtrees, and parallelize the ordered NR-tree-based algorithm on MapReduce framework. Extensive experiments are conducted on both real and synthetic datasets to verify the effectiveness, efficiency and scalability of our techniques.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (61966036, 61662086, 61762090), and the Project of Innovative Research Team of Yunnan Province (2018HC019).

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Authors and Affiliations

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

    Peizhong Yang, Lizhen Wang, Xiaoxuan Wang & Lihua Zhou

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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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  4. Lihua Zhou
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Correspondence to Lizhen Wang.

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Yang, P., Wang, L., Wang, X. et al. Efficient discovery of co-location patterns from massive spatial datasets with or without rare features. Knowl Inf Syst 63, 1365–1395 (2021). https://doi.org/10.1007/s10115-021-01559-3

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