Efficient Mining of Correlation Patterns in Spatial Point Data

  • Marko Salmenkivi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4213)


We address the problem of analyzing spatial correlation between event types in large point data sets. Collocation rules are unsatisfactory, when confidence is not a sufficiently accurate interestingness measure, and Monte Carlo testing is infeasible, when the number of event types is large. We introduce an algorithm for mining correlation patterns, based on a non-parametric bootstrap test that, however, avoids the actual resampling by scanning each point and its distances to the events in the neighbourhood. As a real data set we analyze a large place name data set, the set of event types consisting of different linguistic features that appear in the place names. Experimental results show that the algorithm can be applied to large data sets with hundreds of event types.


Synthetic Data Event Type Correlation Pattern Point Pattern Linguistic Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Davison, A.C., Hinkley, D.V.: Bootstrap Methods and their Application. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, Cambridge (1997)MATHGoogle Scholar
  2. 2.
    Diggle, P.J.: Statistical Analysis of Spatial Point Patterns. In: Mathematics in Biology. Academic Press, London (1983)Google Scholar
  3. 3.
    Huang, Y., Shekhar, S., Xiong, H.: Discovering Colocation Patterns from Spatial Data Sets: A General Approach. IEEE Transactions on Knowledge and Data Engineering 16(12), 1472–1485 (2004)CrossRefGoogle Scholar
  4. 4.
    Huang, Y., Xiong, H., Shekhar, S., Pei, J.: Mining confident co-location rules without a support threshold. In: Proc. 2003 ACM Symposium on Applied computing, Melbourne, Florida, pp. 497–501 (2003)Google Scholar
  5. 5.
    Leino, A., Mannila, H., Pitkänen, R.: Rule discovery and probabilistic modeling for onomastic data. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 291–302. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  6. 6.
    Salmenkivi, M.: Evaluating attraction in spatial point patterns with an application in the field of cultural history. In: Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM 2004), Brighton, UK, November 2004, pp. 511–514 (2004)Google Scholar
  7. 7.
    Salmenkivi, M., Hyvönen, S., Leino, A., Tuominen, H.: Computational survey of clustering in Finnish place name elements. In: Proc. of 22nd International Conference on Onomastic Sciences, ICOS XXII, Pisa, Italy, August–September (2005)Google Scholar
  8. 8.
    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. Springer, Heidelberg (2001)Google Scholar
  9. 9.
    Zhang, X., Mamoulis, N., Cheung, D., Shou, Y.: Fast mining of spatial collocations. In: Proc. 10th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Seattle, Washington, pp. 384–393 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Marko Salmenkivi
    • 1
  1. 1.Helsinki Institute for Information Technology, Basic Research Unit, Department of Computer ScienceUniversity of HelsinkiFinland

Personalised recommendations