SSCP: Mining Statistically Significant Co-location Patterns

  • Sajib Barua
  • Jörg Sander
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6849)

Abstract

Co-location pattern discovery searches for subsets of spatial features whose instances are often located at close spatial proximity. Current algorithms using user specified thresholds for prevalence measures may report co-locations even if the features are randomly distributed. In our model, we look for subsets of spatial features which are co-located due to some form of spatial dependency but not by chance. We first introduce a new definition of co-location patterns based on a statistical test. Then we propose an algorithm for finding such co-location patterns where we adopt two strategies to reduce computational cost compared to a naïve approach based on simulations of the data distribution. We propose a pruning strategy for computing the prevalence measures. We also show that instead of generating all instances of an auto-correlated feature during a simulation, we could generate a reduced number of instances for the prevalence measure computation. We evaluate our algorithm empirically using synthetic and real data and compare our findings with the results found in a state-of-the-art co-location mining algorithm.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules in Large Databases. In: Proc. VLDB, pp. 487–499 (1994)Google Scholar
  2. 2.
    Celik, M., Shekhar, S., Rogers, J.P., Shine, J.A.: Mixed-Drove Spatiotemporal Co-occurence Pattern Mining. IEEE TKDE 20(10), 1322–1335 (2008)Google Scholar
  3. 3.
    Cressie, N.A.C.: Statistics for Spatial Data. Wiley, Chichester (1993)MATHGoogle Scholar
  4. 4.
    Diggle, P.J., Gratton, R.J.: Monte Carlo Methods of Inference for Implicit Statistical Models. J. of the Royal Statist. Society, Series B 46(2), 193–227 (1984)MATHGoogle Scholar
  5. 5.
    Harkness, R.D., Isham, V.: A Bivariate Spatial Point Pattern of Ants’ Nests. J. of the Royal Statist. Society, Series C (Appl. Statist.) 32(3), 293–303 (1983)Google Scholar
  6. 6.
    Huang, Y., Shekhar, S., Xiong, H.: Discovering Colocation Patterns from Spatial Data Sets: A General Approach. IEEE TKDE 16(12), 1472–1485 (2004)Google Scholar
  7. 7.
    Illian, J., Penttinen, A., Stoyan, H., Stoyan, D.: Statistical Analysis and Modelling of Spatial Point Patterns. Wiley, Chichester (2008)MATHGoogle Scholar
  8. 8.
    Koperski, K., Han, J.: Discovery of Spatial Association Rules in Geographic Information Databases. In: Egenhofer, M.J., Herring, J.R. (eds.) SSD 1995. LNCS, vol. 951, pp. 47–66. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  9. 9.
    Mane, S., Murray, C., Shekhar, S., Srivastava, J., Pusey, A.: Spatial Clustering of Chimpanzee Locations for Neighborhood Identification. In: Proc. ICDM, pp. 737–740 (2005)Google Scholar
  10. 10.
    Morimoto, Y.: Mining Frequent Neighboring Class Sets in Spatial Databases. In: Proc. SIGKDD, pp. 353–358 (2001)Google Scholar
  11. 11.
    Ripley, B.: The Second-Order Analysis of Stationary Point Processes. J. of Appl. Probability 13(2), 255–266 (1976)CrossRefMATHGoogle Scholar
  12. 12.
    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
  13. 13.
    Xiao, X., Xie, X., Luo, Q., Ma, W.Y.: Density Based Co-location Pattern Discovery. In: Proc. GIS, pp. 250–259 (2008)Google Scholar
  14. 14.
    Yoo, J.S., Shekhar, S.: A Partial Join Approach for Mining Co-location Patterns. In: Proc. GIS, pp. 241–249 (2004)Google Scholar
  15. 15.
    Yoo, J.S., Shekhar, S.: A Joinless Approach for Mining Spatial Colocation Patterns. IEEE TKDE 18(10), 1323–1337 (2006)Google Scholar
  16. 16.
    Yoo, J.S., Shekhar, S., Kim, S., Celik, M.: Discovery of Co-evolving Spatial Event Sets. In: Proc. SDM, pp. 306–315 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sajib Barua
    • 1
  • Jörg Sander
    • 1
  1. 1.Dept. of Computing ScienceUniversity of AlbertaEdmontonCanada

Personalised recommendations