Spatiotemporal Co-occurrence Rules

  • Karthik Ganesan Pillai
  • Rafal A. Angryk
  • Juan M. Banda
  • Tim Wylie
  • Michael A. Schuh
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 241)


Spatiotemporal co-occurrence rules (STCORs) discovery is an important problem in many application domains such as weather monitoring and solar physics, which is our application focus. In this paper, we present a general framework to identify STCORs for continuously evolving spatiotemporal events that have extended spatial representations. We also analyse a set of anti-monotone (monotonically non-increasing) and non anti-monotone measures to identify STCORs. We then validate and evaluate our framework on a real-life data set and report results of the comparison of the number candidates needed to discover actual patterns, memory usage, and the number of STCORs discovered using the anti-monotonic and non anti-monotonic measures.


spatiotemporal events extended spatial representations spatiotemporal co-occurrence rules 


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  1. 1.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. SIGMOD Rec. 22(2), 207–216 (1993)CrossRefGoogle Scholar
  2. 2.
    Cao, H., Mamoulis, N., Cheung, D.W.: Discovery of collocation episodes in spatiotemporal data. In: The 6th Intern. Conf. on Data Mining, DC, pp. 823–827 (2006)Google Scholar
  3. 3.
    Celik, M., Shekhar, S., Rogers, J.P., Shine, J.A., Yoo, J.S.: Mixed-drove spatio-temporal co-occurence pattern mining: A summary of results. In: The 6th Intern. Conf. on Data Mining, DC, pp. 119–128 (2006)Google Scholar
  4. 4.
    Egghe, L., Michel, C.: Strong similarity measures for ordered sets of documents in information retrieval. Inf. Process. Manag. 38(6), 823–848 (2002)MATHCrossRefGoogle Scholar
  5. 5.
    HEK (January 2012),
  6. 6.
    Huang, Y., Shekhar, S., Xiong, H.: Discovering colocation patterns from spatial data sets: a general approach. Trans. on Know. and Data Eng., 1472–1485 (2004)Google Scholar
  7. 7.
    Manning, C.D., Schütze, H.: Foundations of statistical natural language processing. MIT Press, Cambridge (1999)MATHGoogle Scholar
  8. 8.
    Patel, D.: Interval-orientation patterns in spatio-temporal databases. In: Bringas, P.G., Hameurlain, A., Quirchmayr, G. (eds.) DEXA 2010, Part I. LNCS, vol. 6261, pp. 416–431. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Pillai, K.G., Angryk, R.A., Banda, J.M., Schuh, M.A., Wylie, T.: Spatio-temporal co-occurrence pattern mining in data sets with evolving regions. In: ICDM Workshops, pp. 805–812 (2012)Google Scholar
  10. 10.
    Schuh, M.A., Angryk, R.A., Pillai, K.G., Banda, J.M., Martens, P.C.: A large-scale solar image dataset with labeled event regions. In: Int. Conf. on Image Processing, ICIP (2013)Google Scholar
  11. 11.
    Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston (2005)Google Scholar
  12. 12.
    Taylor, P.: Quantitative Methods in Geography: An Introduction to Spatial Analysis. Houghton Mifflin (1977)Google Scholar
  13. 13.
    Wang, J., Hsu, W., Lee, M.L.: A framework for mining topological patterns in spatio-temporal databases. In: CIKM 2005, pp. 429–436. ACM, New York (2005)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 (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Karthik Ganesan Pillai
    • 1
  • Rafal A. Angryk
    • 1
  • Juan M. Banda
    • 1
  • Tim Wylie
    • 1
  • Michael A. Schuh
    • 1
  1. 1.Montana State UniversityBozemanUSA

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