Mining Co-locations from Continuously Distributed Uncertain Spatial Data

  • Bozhong Liu
  • Ling Chen
  • Chunyang Liu
  • Chengqi Zhang
  • Weidong Qiu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9931)


A co-location pattern is a group of spatial features whose instances tend to locate together in geographic space. While traditional co-location mining focuses on discovering co-location patterns from deterministic spatial data sets, in this paper, we study the problem in the context of continuously distributed uncertain data. In particular, we aim to discover co-location patterns from uncertain spatial data where locations of spatial instances are represented as multivariate Gaussian distributions. We first formulate the problem of probabilistic co-location mining based on newly defined prevalence measures. When the locations of instances are represented as continuous variables, the major challenges of probabilistic co-location mining lie in the efficient computation of prevalence measures and the verification of the probabilistic neighborhood relationship between instances. We develop an effective probabilistic co-location mining framework integrated with optimization strategies to address the challenges. Our experiments on multiple datasets demonstrate the effectiveness of the proposed algorithm.


Covariance Transportation 


  1. 1.
    Mining Co-locations from Continuously Distributed Uncertain Spatial Data (2016).
  2. 2.
    Allenby, R., Slomson, A.: How to Count: An Introduction to Combinatorics. Discrete Mathematics and Its Applications, 2nd edn. CRC Press (2010)Google Scholar
  3. 3.
    Bernecker, T., Kriegel, H.-P., Renz, M., Verhein, F., Züfle, A.: Probabilistic frequent itemset mining in uncertain databases. In: KDD, pp. 119–128 (2009)Google Scholar
  4. 4.
    Dong, T., Xiao, C., Guo, X., Ishikawa, Y.: Processing probabilistic range queries over Gaussian-based uncertain data. In: Nascimento, M.A., Sellis, T., Cheng, R., Sander, J., Zheng, Y., Kriegel, H.-P., Renz, M., Sengstock, C. (eds.) SSTD 2013. LNCS, vol. 8098, pp. 410–428. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  5. 5.
    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
  6. 6.
    Ishikawa, Y., Iijima, Y., Yu, J.X.: Spatial range querying for gaussian-based imprecise query objects. In ICDE, pp. 676–687 (2009)Google Scholar
  7. 7.
    Li, F., Cheng, D., Hadjieleftheriou, M., Kollios, G., Teng, S.-H.: On trip planning queries in spatial databases. In: Medeiros, C.B., Egenhofer, M., Bertino, E. (eds.) SSTD 2005. LNCS, vol. 3633, pp. 273–290. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Liu, Z., Huang, Y.: Mining co-locations under uncertainty. In: Nascimento, M.A., Sellis, T., Cheng, R., Sander, J., Zheng, Y., Kriegel, H.-P., Renz, M., Sengstock, C. (eds.) SSTD 2013. LNCS, vol. 8098, pp. 429–446. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  9. 9.
    Niedermayer, J., Züfle, A., Emrich, T., Renz, M., Mamoulis, N., Chen, L., Kriegel, H.-P.: Probabilistic nearest neighbor queries on uncertain moving object trajectories. PVLDB 7(3), 205–216 (2013)Google Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics (Intelligent Robotics and Autonomous Agents). The MIT Press, Cambridge (2005)MATHGoogle Scholar
  12. 12.
    Wang, L., Wu, P., Chen, H.: Finding probabilistic prevalent colocations in spatially uncertain data sets. IEEE Trans. Knowl. Data Eng. 25(4), 790–804 (2013)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Xia, Y., Yang, Y., Chi, Y.: Mining association rules with non-uniform privacy concerns. In: DMKD, pp. 27–34 (2004)Google Scholar
  14. 14.
    Xiong, H., Shekhar, S., Huang, Y., Kumar, V., Ma, X., Yoo, J.S.: A framework for discovering co-location patterns in data sets with extended spatial objects. In: SDM, pp. 78–89 (2004)Google Scholar
  15. 15.
    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
  16. 16.
    Yoo, J.S., Shekhar, S., Celik, M.: A join-less approach for co-location pattern mining: a summary of results. In: ICDM, pp. 813–816 (2005)Google Scholar
  17. 17.
    Zhang, X., Mamoulis, N., Cheung, D.W., Shou, Y.: Fast mining of spatial collocations. In: KDD, pp. 384–393 (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Bozhong Liu
    • 1
    • 2
  • Ling Chen
    • 2
  • Chunyang Liu
    • 2
  • Chengqi Zhang
    • 2
  • Weidong Qiu
    • 1
  1. 1.School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Centre for Quantum Computation and Intelligent SystemsUniversity of Technology SydneySydneyAustralia

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