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GAMInG – A Framework for Generalization of Association Mining via Information Granulation

  • Ying Xie
  • Vijay V. Raghavan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3066)

Abstract

Rather than finding new association-mining types one at a time, in this paper, we propose a framework, which is called Generalization of Association Mining via Information Granulation (GAMInG), based on which new association-mining types capable of discovering new patterns hidden in data can be systematically defined.

Keywords

Association Rule Binary Relation Information Table Association Pattern Information Granulation 
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., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proc. ACM-SIGMOD Int. Conf. on Management of Data, Washington, DC (1993)Google Scholar
  2. 2.
    Lu, H., Feng, L., Han, J.: Beyond intra-transaction association analysis: Mining multidimensional inter-transaction association rules. ACM Trans. on Information Systems 18(4) (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Ying Xie
    • 1
  • Vijay V. Raghavan
    • 1
  1. 1.The Center for Advanced Computer StudiesUniversity of Louisiana at LafayetteLafayetteUSA

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