Abstract
Spatial co-location patterns, whose instances are frequently located together in geography, are particularly valuable for discovering spatial dependencies. Since its inception, lots of co-location pattern mining algorithms have been developed, but the computational cost remains prohibitively expensive with large data size. In this work, we propose to parallelize joinless algorithm on MapReduce framework. Our approach partitions computation in such a way that each machine independently executes joinless algorithm to finish a group of mining tasks. Such partitioning eliminates computational dependencies and reduces communication cost between machines. Moreover, a novel pruning technique is suggested to improve mining performance. The experimental results on synthetic and real-world data sets show that the parallel joinless algorithm is efficient and scalable.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Shekhar, S., Huang, Y.: Discovering spatial co-location patterns: a summary of results. In: Jensen, C.S., Schneider, M., Seeger, B., Tsotras, V.J. (eds.) SSTD 2001. LNCS, vol. 2121, pp. 236–256. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-47724-1_13
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)
Yu, W.: Spatial co-location pattern mining for location-based services in road networks. Expert Syst. Appl. 46(2016), 324–335 (2016)
Wang, L., Bao, X., Zhou, L.: Redundancy reduction for prevalent co-location patterns. IEEE Trans. Knowl. Data Eng. 30(1), 142–155 (2018)
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)
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)
Yoo, J.S., Shekhar, S.: A joinless approach for mining spatial colocation patterns. IEEE Trans. Knowl. Data Eng. 18(10), 1323–1337 (2006)
Wang, L., Bao, Y., Lu, J., Yip, J.: A new join-less approach for co-location pattern mining. In: 8th IEEE International Conference on Computer and Information Technology (CIT 2008), pp. 197–202 (2008)
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)
Yao, X., Peng, L., Yang, L., Chi, T.: A fast space-saving algorithm for maximal co-location pattern mining. Expert Syst. Appl. 63(2016), 310–323 (2016)
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), the Project of Innovative Research Team of Yunnan Province (2018HC019), and the Project of Yunnan University Graduate Student Scientific Research (YDY17110).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Yang, P., Wang, L., Wang, X., Fang, Y. (2018). A Parallel Joinless Algorithm for Co-location Pattern Mining Based on Group-Dependent Shard. In: Hacid, H., Cellary, W., Wang, H., Paik, HY., Zhou, R. (eds) Web Information Systems Engineering – WISE 2018. WISE 2018. Lecture Notes in Computer Science(), vol 11234. Springer, Cham. https://doi.org/10.1007/978-3-030-02925-8_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-02925-8_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02924-1
Online ISBN: 978-3-030-02925-8
eBook Packages: Computer ScienceComputer Science (R0)