Mining Co-locations from Spatially Uncertain Data with Probability Intervals

  • Lizhen Wang
  • Peng Guan
  • Hongmei Chen
  • Qing Xiao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7901)


Uncertain data are inherent in many applications, and are usually described by precise probabilities. However, it is difficult to obtain precise probabilities over uncertain data in applications. This paper studies the problem of mining co-locations from spatially uncertain data with probability intervals. Firstly, it defines the possible world model with probability intervals, and proves that probability intervals of all possible worlds are feasible. Secondly, based on the feasible probability interval, it converts the probability intervals of possible worlds into point probabilities. Further, it defines the related concepts of probabilistic prevalent co-locations. Thirdly, it gives two lemmas for optimizing the computation of prevalence point probability of a candidate co-location. Further, it proves the closure property of prevalence point probability. Finally, the experiments on synthetic and real data sets show that the algorithms are effective and significant.


Uncertain data mining probability interval possible world probabilistic prevalent co-location 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wang, L., Zhou, L., Chen, H., et al.: The principle and applications of data warehouses and data mining, 2nd edn. Science press, Beijing (2009)Google Scholar
  2. 2.
    Klir, G.J., Watson, T.J.: Uncertainty and information measures for imprecise probabilities: an overview. In: The First International Symposium on Imprecise Probabilities and Their Applications (ISIPTA 1999), Ghent, Belgium, pp. 234–240 (1999)Google Scholar
  3. 3.
    Huang, Y., Shekhar, S., Xiong, H.: Discovering co-location patterns from spatial data sets: a general approach. IEEE Transactions on Knowledge and Data Engineering (TKDE) 16(12), 1472–1485 (2004)CrossRefGoogle Scholar
  4. 4.
    Yoo, J.S., Shekhar, S.: A join-less approach for co-location pattern mining: a summary of results. IEEE Transactions on Knowledge and Data Engineering (TKDE) 18(10), 1323–1337 (2006)CrossRefGoogle Scholar
  5. 5.
    Wang, L., Bao, Y., Lu, J., Yip, J.: A new join-less approach for co-location pattern mining. In: The 8th IEEE International Conference on Computer and Information Technology (CIT 2008), pp. 197–202. IEEE Press, New York (2008)CrossRefGoogle Scholar
  6. 6.
    Wang, L., Zhou, L., Lu, J., Yip, J.: An order-clique-based approach for mining maximal co-locations. Information Sciences 179(19), 3370–3382 (2009)zbMATHCrossRefGoogle Scholar
  7. 7.
    Ouyang, Z., Wang, L., Chen, H.: Mining spatial co-location patterns for fuzzy objects. Chinese Journal of Computers 34(10), 1947–1955 (2011)CrossRefGoogle Scholar
  8. 8.
    Huang, Y., Pei, J., Xiong, H.: Mining co-location patterns with rare events from spatial data sets. Geoinformatica 10(3), 239–260 (2006)CrossRefGoogle Scholar
  9. 9.
    Feng, L., Wang, L., Gao, S.: A new approach of mining co-location patterns in spatial datasets with rare features. Journal of Nanjing University (Natural Sciences) 48(1), 99–107 (2012)Google Scholar
  10. 10.
    Lu, Y., Wang, L., Zhang, X.: Mining frequent co-location patterns from uncertain data. Journal of Frontiers of Computer Science and Technology 3(6), 656–664 (2009)Google Scholar
  11. 11.
    Lu, Y., Wang, L., Chen, H., et al.: Spatial co-location patterns mining over uncertain data based on possible worlds. Journal of Computer Research and Development, 47 (suppl.), 215–221 (2010)Google Scholar
  12. 12.
    Wang, L., Wu, P., Chen, H.: Finding probabilistic prevalent co-locations in spatially uncertain data sets. IEEE Transactions on Knowledge and Data Engineering (TKDE) 25(4), 790–804 (2013)CrossRefGoogle Scholar
  13. 13.
    Abellan, J., Moral, S.: Maximum of entropy for credal sets. International Journal of Uncertainty, Fuzziness and Knowledge Based Systems 11(5), 587–597 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    He, D., Zhou, R.: Study on methods of decision-making under interval probability. Journal of Systems and Management 19(2), 210–214 (2010)MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Lizhen Wang
    • 1
  • Peng Guan
    • 1
  • Hongmei Chen
    • 1
  • Qing Xiao
    • 1
  1. 1.Department of Computer Science and Engineering, School of Information Science and EngineeringYunnan UniversityKunmingChina

Personalised recommendations