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A Novel Approach of Multilevel Positive and Negative Association Rule Mining for Spatial Databases

  • L. K. Sharma
  • O. P. Vyas
  • U. S. Tiwary
  • R. Vyas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3587)

Abstract

Spatial data mining is a demanding field since huge amounts of spatial data have been collected in various applications, ranging form Remote Sensing to GIS, Computer Cartography, Environmental Assessment and Planning. Although there have been efforts for spatial association rule mining, but mostly researchers discuss only the positive spatial association rules; they have not considered the spatial negative association rules. Negative association rules are very useful in some spatial problems and are capable of extracting some useful and previously unknown hidden information. We have proposed a novel approach of mining spatial positive and negative association rules. The approach applies multiple level spatial mining methods to extract interesting patterns in spatial and/or non-spatial predicates. Data and spatial predicates/association-ship are organized as set hierarchies to mine them level-by-level as required for multilevel spatial positive and negative association rules. A pruning strategy is used in our approach to efficiently reduce the search space. Further efficiency is gained by interestingness measure.

Keywords

Association Rule Spatial Database Association Rule Mining Spatial Object Pruning Strategy 
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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References

  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithm for mining association rules. In: Proc. 1994 Int. Conf. VLDB Santiago, Chile, September 1994, pp. 487–499 (1994)Google Scholar
  2. 2.
    Guetting, R.H.: An Introduction to Spatial Database Systems. Special Issue on Spatial Database System of the VLDB Journal 3(4) (October 1994)Google Scholar
  3. 3.
    Han, J., Fu, Y.: Discovery of Multiple Level association rules from large database. In: Proc. of the Int. Conf. VLDB, pp. 420–431 (1995)Google Scholar
  4. 4.
    Han, J., Pei, J., Yin, Y., Mao, R.: Mining Frequent Patterns with out Candidate Generation: A Frequent Pattern Tree Approach. Kluwer Publication, Netherlands (2003)Google Scholar
  5. 5.
    Malerba, D., Lisi, F.A.: An ILP method for spatial association rule mining. Working notes of the first workshop on Multi Relational Data mining, Freiburg, Germany, pp. 18–29 (2001)Google Scholar
  6. 6.
    Malerba, D., Lisi, F.A.: Analisa Appice, Francesco. In: Mining Spatial Association Rules in Census Data: A Relational Approach (2001)Google Scholar
  7. 7.
    Shekhar, S., Chawla, S., Ravadam, S., Liu, X., Lu, C.: Spatial Databases- Accomplishments and Research Needs. IEEE Transactions on Knowledge and Data Engineering 11(1) (1999)Google Scholar
  8. 8.
    Smith, G.B., Bridge, S.M.: Fuzzy Spatial Data Mining. IEEE Transactions on Knowledge and Data Engineering (2002)Google Scholar
  9. 9.
    Wu, X., Zhang, C., Zhang, S.: Efficient Mining of Both Positive and Negative Association rule. ACM Tran. On Information System 22(3), 381–405 (2004)CrossRefGoogle Scholar
  10. 10.
    Dunham, M.H.: Data Mining Introductory and Advance Topics. Pearson Education Inc., London (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • L. K. Sharma
    • 1
  • O. P. Vyas
    • 1
  • U. S. Tiwary
    • 2
  • R. Vyas
    • 1
  1. 1.School of Studies in Computer SciencePt. Ravishankar Shukla UniversityRaipurIndia
  2. 2.Indian Institute of Information TechnologyAllahabadIndia

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