Advertisement

Mining Spatial Rules by Finding Empty Intervals in Data

  • Alexandr Savinov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2773)

Abstract

Most rule induction algorithms including those for association rule mining use high support as one of the main measures of interestingness. In this paper we follow an opposite approach and describe an algorithm, called Optimist, which finds all largest empty intervals in data and then transforms then into the form of multiple-valued rules. It is demonstrated how this algorithm can be applied to mining spatial rules where data involves both geographic and thematic properties. Data preparation (spatial feature generation), data analysis and knowledge postprocessing stages were implemented in the SPIN! spatial data mining system where this algorithm is one of its components.

Keywords

Association Rule Rule Induction Empty Interval Thematic Property Enumeration District 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Savinov, A.A.: Mining possibilistic set-valued rules by generating prime disjunctions. In: Żytkow, J.M., Rauch, J. (eds.) PKDD 1999. LNCS (LNAI), vol. 1704, pp. 536–541. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  2. 2.
    Savinov, A.A.: Application of multi-dimensional fuzzy analysis to decision making. In: Roy, R., Furuhashi, T., Chawdhry, P.K. (eds.) Advances in Soft Computing — Engineering Design and Manufacturing. Springer, London (1999)Google Scholar
  3. 3.
    Savinov, A.A.: An algorithm for induction of possibilistic set-valued rules by finding prime disjunctions. In: Suzuki, Y., Ovaska, S.J., Furuhashi, T., Roy, R., Dote, Y. (eds.) Soft computing in industrial applications. Springer, London (2000)Google Scholar
  4. 4.
    Liu, B., Ku, L.-P., Hsu, W.: Discovering Interesting Holes in Data. In: Proceedings of Fifteenth International Joint Conference on Artificial Intelligence (IJCAI 1997), Nagoya, Japan, August 23-29, pp. 930–935 (1997)Google Scholar
  5. 5.
    Liu, B., Wang, K., Mun, L.-F., Qi, X.-Z.: Using Decision Tree Induction for Discovering Holes in Data. In: Lee, H.-Y. (ed.) PRICAI 1998. LNCS, vol. 1531, pp. 182–193. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  6. 6.
    Ku, L.-P., Liu, B., Hsu, W.: Discovering Large Empty Maximal Hyper-rectangles in Multi-dimensional Space. Technical Report, Department of Information Systems and Computer Science (DCOMP), National University of Singapore (1997)Google Scholar
  7. 7.
    Edmonds, J., Gryz, J., Liang, D., Miller, R.J.: Mining for Empty Rectangles in Large Data Sets. In: Van den Bussche, J., Vianu, V. (eds.) ICDT 2001. LNCS, vol. 1973, pp. 174–188. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  8. 8.
    Orlowski, M.: A New Algorithm for the Largest Empty Rectangle Problem. Algorithmica 5(1), 65–73 (1990)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Chazelle, B., Drysdale, R.L., Lee, D.T.: Computing the largest empty rectangle. SIAM J. Comput. 15, 300–315 (1986)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    May, M., Savinov, A.: An Architecture for the SPIN! Spatial Data Mining Platform. In: Proc. New Techniques and Technologies for Statistics, NTTS 2001, pp. 467–472 (2001), EurostatGoogle Scholar
  11. 11.
    May, M., Savinov, A.: An integrated platform for spatial data mining and interactive visual analysis. In: Data Mining 2002, Bologna, Italy, September 25-27, pp. 51–60 (2002)Google Scholar
  12. 12.
    Andrienko, G., Andrienko, N., Savinov, A.: Choropleth Maps: Classification revisited. In: Proceedings ICA 2001, Beijing, China, vol. 2, pp. 1209–1219 (20001)Google Scholar
  13. 13.
    Andrienko, N., Andrienko, G., Savinov, A., Voss, H., Wettschereck, D.: Exploratory Analysis of Spatial Data Using Interactive Maps and Data Mining. Cartography and Geographic Information Science 2001 28(3), 151–165 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Alexandr Savinov
    • 1
  1. 1.Fraunhofer Institute for Autonomous Intelligent SystemsSankt-AugustinGermany

Personalised recommendations