Algorithms for Association Rules

  • Markus Hegland
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2600)


Association rules are “if-then rules ”with two measures which quantify the support and confidence of the rule for a given data set. Having their origin in market basked analysis,association rules are now one of the most popular tools in data mining.This popularity is to a large part due to the availability of efficient algorithms following from the development of the Apriori algorithm.

We will review the basic Apriori algorithm and discuss variants for distributed data,inclusion of constraints and data taxonomies.The review ends with an outlook on tools which have the potential to deal with long itemsets and considerably reduce the amount of (uninteresting)itemsets returned.The discussion will focus on the problem of finding frequent itemsets.


Association Rule Frequent Itemsets Association Rule Mining Support Threshold Formal Concept Analysis 
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 2003

Authors and Affiliations

  • Markus Hegland
    • 1
  1. 1.Australian National UniversityCanberraAustralia

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