Composition of Mining Contexts for Efficient Extraction of Association Rules

  • Cheikh Talibouya Diop
  • Arnaud Giacometti
  • Dominique Laurent
  • Nicolas Spyratos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2287)


Association rule mining often requires the repeated execution of some extraction algorithm for different values of the support and confidence thresholds, as well as for different source datasets. This is an expensive process, even if we use the best existing algorithms. Hence the need for incremental mining, whereby mining results already obtained can be used to accelerate subsequent steps in the mining process.

In this paper, we present an approach for the incremental mining of multidimensional association rules. In our approach, association rule mining takes place in a mining context which specifies the form of rules to be mined. Incremental mining is obtained by combining mining contexts using relational algebra operations.


Association Rule Selection Condition Association Rule Mining Relational Algebra Support Threshold 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Cheikh Talibouya Diop
    • 1
    • 3
  • Arnaud Giacometti
    • 1
  • Dominique Laurent
    • 1
  • Nicolas Spyratos
    • 2
  1. 1.LIUniversité de ToursBloisFrance
  2. 2.LRIUniversité Paris 11Orsay CedexFrance
  3. 3.Université Gaston BergerSaint-LouisSenegal

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