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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Huang, Y., Shekhar, S., Xiong, H.: Discovering colocation patterns from spatial data sets: a general approach. IEEE Trans. Knowl. Data Eng. 16(12), 1472–1485 (2004)
Yoo, J.S., Shekhar, S.: A joinless approach for mining spatial colocation patterns. IEEE Trans. Knowl. Data Eng. 18(10), 1323–1337 (2006)
Yoo, J.S., Shekhar, S.: A partial join approach for mining co-location patterns. In: The 12th Annual ACM International Workshop on Geographic Information Systems, pp. 241–249 (2004)
Wang, L., Bao, X., Zhou, L.: Redundancy reduction for prevalent co-location patterns. IEEE Trans. Knowl. Data Eng. 30(1), 142–155 (2018)
Xiao, X., Xie, X., Luo, Q., Ma, W.: Density based co-location pattern discovery. In: 16th ACM SIGSPATIAL, pp. 1–10 (2008)
Lin, Z., Lim, S.J.: Fast spatial co-location mining without cliqueness checking. In: International Conference on Information and Knowledge Management, pp. 1461–1462 (2008)
Yoo, J.S., Boulware, D., Kimmey, D.: A parallel spatial co-location mining algorithm based on MapReduce. In: IEEE International Congress on Big Data, pp. 25–31 (2014)
Wang, L., Bao, X., Chen, H., Cao, L.: Effective lossless condensed representation and discovery of spatial co-location patterns. Inf. Sci. 436–437(2018), 197–213 (2018)
Acknowledgement
This work is supported by the National Natural Science Foundation of China (61472346, 61662086, 61762090), the Natural Science Foundation of Yunnan Province (2015FB114, 2016FA026), and the Project of Innovative Research Team of Yunnan Province.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Yang, P., Wang, L., Wang, X. (2018). A Parallel Spatial Co-location Pattern Mining Approach Based on Ordered Clique Growth. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10827. Springer, Cham. https://doi.org/10.1007/978-3-319-91452-7_47
Download citation
DOI: https://doi.org/10.1007/978-3-319-91452-7_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91451-0
Online ISBN: 978-3-319-91452-7
eBook Packages: Computer ScienceComputer Science (R0)