Advertisement

A Parallel Spatial Co-location Pattern Mining Approach Based on Ordered Clique Growth

  • Peizhong Yang
  • Lizhen Wang
  • Xiaoxuan Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10827)

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.

Keywords

Spatial data mining Co-location patterns Ordered clique Parallel algorithm MapReduce 

Notes

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.

References

  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    Yoo, J.S., Shekhar, S.: A joinless approach for mining spatial colocation patterns. IEEE Trans. Knowl. Data Eng. 18(10), 1323–1337 (2006)CrossRefGoogle Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    Wang, L., Bao, X., Zhou, L.: Redundancy reduction for prevalent co-location patterns. IEEE Trans. Knowl. Data Eng. 30(1), 142–155 (2018)CrossRefGoogle Scholar
  5. 5.
    Xiao, X., Xie, X., Luo, Q., Ma, W.: Density based co-location pattern discovery. In: 16th ACM SIGSPATIAL, pp. 1–10 (2008)Google Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    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)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information Science and EngineeringYunnan UniversityKunmingChina

Personalised recommendations