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TRARM-RelSup: Targeted Rare Association Rule Mining Using Itemset Trees and the Relative Support Measure

  • Jennifer Lavergne
  • Ryan Benton
  • Vijay V. Raghavan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7661)

Abstract

The goal of association mining is to find potentially interesting rules in large repositories of data. Unfortunately using a minimum support threshold, a standard practice to improve the association mining processing complexity, can allow some of these rules to remain hidden. This occurs because not all rules which have high confidence have a high support count. Various methods have been proposed to find these low support rules, but the resulting increase in complexity can be prohibitively expensive. In this paper, we propose a novel targeted association mining approach to rare rule mining using the itemset tree data structure (aka TRARM-RelSup). This algorithm combines the efficiency of targeted association mining querying with the capabilities of rare rule mining; this results in discovering a more focused, standard and rare rules for the user, while keeping the complexity manageable.

Keywords

data mining association mining targeted association mining itemset tree rare association rule mining 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jennifer Lavergne
    • 1
  • Ryan Benton
    • 1
  • Vijay V. Raghavan
    • 1
  1. 1.The Center for Advanced Computer StudiesUniversity of Louisiana at LafayetteLafayetteUSA

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