Data Mining and Knowledge Discovery

, Volume 8, Issue 1, pp 25–51 | Cite as

Tree Structures for Mining Association Rules

  • Frans Coenen
  • Graham Goulbourne
  • Paul Leng


A well-known approach to Knowledge Discovery in Databases involves the identification of association rules linking database attributes. Extracting all possible association rules from a database, however, is a computationally intractable problem, because of the combinatorial explosion in the number of sets of attributes for which incidence-counts must be computed. Existing methods for dealing with this may involve multiple passes of the database, and tend still to cope badly with densely-packed database records. We describe here a class of methods we have introduced that begin by using a single database pass to perform a partial computation of the totals required, storing these in the form of a set enumeration tree, which is created in time linear to the size of the database. Algorithms for using this structure to complete the count summations are discussed, and a method is described, derived from the well-known Apriori algorithm. Results are presented demonstrating the performance advantage to be gained from the use of this approach. Finally, we discuss possible further applications of the method.

association rules set-enumeration tree 


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Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Frans Coenen
    • 1
  • Graham Goulbourne
    • 1
  • Paul Leng
    • 1
  1. 1.Department of Computer ScienceThe University of LiverpoolLiverpoolUK

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