Nature-Inspired Approaches to Mining Trend Patterns in Spatial Databases

  • Ashkan Zarnani
  • Masoud Rahgozar
  • Caro Lucas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)


Large repositories of spatial data have been formed in various applications such as Geographic Information Systems (GIS), environmental studies, banking, etc. The increasing demand for knowledge residing inside these databases has attracted much attention to the field of Spatial Data Mining. Due to the common complexity and huge size of spatial databases the aspect of efficiency is of the main concerns in spatial knowledge discovery algorithms. In this paper, we introduce two novel nature-inspired algorithms for efficient discovery of spatial trends, as one of the most valuable patterns in spatial databases. The algorithms are developed using ant colony optimization and evolutionary search. We empirically study and compare the efficiency of the proposed algorithms on a real banking spatial database. The experimental results clearly confirm the improvement in performance and effectiveness of the discovery process compared to the previously proposed methods.


Genetic Algorithm Geographic Information System Spatial Database Spatial Trend Neighborhood Graph 
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.


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  1. Choenni, A.: Design and Implementation of a Genetic-based Algorithm for Data Mining. In: Proc. 26th Int. Conf. VLDB., pp. 33–42 (2000)Google Scholar
  2. Dorigo, M., Bonabeau, E., Theraulaz, G.: Ant Algorithms and Stigmergy. Future Generation Computer Systems 17(8), 851–871 (2000)CrossRefGoogle Scholar
  3. Dorigo, M., Maniezzo, V., Colorni, A.: The Ant System: Optimization by a Colony of Cooperating Agents. IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics 26(1), 29–41 (1996)CrossRefGoogle Scholar
  4. Dorigo, M., Stützle, T.: The Ant Colony Optimization Meta-Heuristic: Algorithms, Applications and Advances. In: Glover, F., Kochenberger, G. (eds.) Handbook of Meta-heuristics. Kluwer Academic Publishers, Dordrecht (2002)Google Scholar
  5. Ester, M., Frommelt, A., Kriegel, H.P., Sander, J.: Spatial Data Mining: Database Primitives, Algorithms and Efficient DBMS Support. Int. Journal of Data Mining and Knowledge Discovery 4(2/3), 193–217 (2000)CrossRefGoogle Scholar
  6. Ester, M., Frommelt, A., Kriegel, H.P., Sander, J.: Algorithms for Characterization and Trend Detection in Spatial Databases. In: Proc. 4th Int. Conf. on Knowledge Discovery and Data Mining, pp. 44–50 (1998)Google Scholar
  7. Ester, M., Kriegel, H.P., Sander, J.: Spatial Data Mining: A Database Approach. In: Proc. 5th Int. Symp. On Large Spatial Databases, pp. 320–328 (1997)Google Scholar
  8. Ester, M., Kriegel, H.P., Sander, J., Xu, X.: Density-Connected Sets and Their Application for Trend Detection in Spatial Databases. In: Proc. 3rd Int. Conf. on Knowledge Discovery and Data Mining, pp. 44–50 (1997)Google Scholar
  9. Freitas, A.A.: A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery. In: Ghosh, A., Tsutsui, S. (eds.) Advances in Evolutionary Computation, pp. 819–846. Springer, Heidelberg (2002)Google Scholar
  10. Koperski, K., Han, J.: Discovery of Spatial Association Rules in Geographic Information Databases. In: Proc. 4th Int. Symp. on Large Spatial Databases, pp. 47–66 (1995)Google Scholar
  11. Koperski, K., Han, J., Stefanovic, N.: An Efficient Two-step Method for Classification of Spatial Data. In: Proc. International Symp. On Spatial Data Handling, pp. 320–328 (1998)Google Scholar
  12. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, New York (1996)MATHGoogle Scholar
  13. Parpinelli, R.S., Lopes, H.S., Freitas, A.A.: Data Mining with an Ant Colony Optimization Algorithm. IEEE Transactions on Evolutionary Computation 6(4), 321–332 (2002)CrossRefGoogle Scholar
  14. Shekhar, S., Schrater, P., Vatsavai, W.R., Wu, W., Chawla, S.: Spatial Contextual Classification and Prediction Models for Mining Geospatial Data. IEEE Transactions on Multmedia 2(4), 174–188 (2002)CrossRefGoogle Scholar
  15. Wang, L., Xie, K., Chen, T., Ma, X.: Efficient Discovery of Multilevel Spatial Association Rules Using Partitions. Information and Software Technology 47(13), 829–840 (2005)CrossRefGoogle Scholar
  16. Zarnani, A., Rahgozar, M., Lucas, C., Memariani, A.: AntTrend: Stigmergetic Discovery of Spatial Trends. To Appear in Proc. 16th Int. Symp. On Methodologies for Intelligent Systems (2006)Google Scholar
  17. Zarnani, A., Rahgozar, M., Lucas, C.: Efficient Discovery of Knowledge form Large Geo-Spatial Databases: An Evolutionary Approach. To Appear in Proc. Int. Conf. on Data Mining(DMIN 2006) Part of the WORDCOMP 2006 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ashkan Zarnani
    • 1
  • Masoud Rahgozar
    • 2
  • Caro Lucas
    • 2
  1. 1.Database Research Group, Faculty of ECE, School of EngineeringUniversity of Tehran 
  2. 2.Control and Intelligent Processing Center of ExcellenceTehranIran

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