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Weighted Association Rule Mining Using Particle Swarm Optimization

  • Russel Pears
  • Yun Sing Koh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7104)

Abstract

Association rule mining is an important data mining task that discovers relationships among items in a transaction database. Most approaches to association rule mining assume that the items within the dataset have a uniform distribution. Therefore, weighted association rule mining (WARM) was introduced to provide a notion of importance to individual items. In previous work most of these approaches require users to assign weights for each item. This is infeasible when we have millions of items in a dataset. In this paper we propose a novel method, Weighted Association Rule Mining using Particle Swarm Optimization (WARM SWARM), which uses particle swarm optimization to assign meaningful item weights for association rule mining.

Keywords

Weighted Items Particle Swarm Optimization Association Rule Mining 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Russel Pears
    • 1
  • Yun Sing Koh
    • 2
  1. 1.School of Computing and Mathematical SciencesAuckland University of TechnologyNew Zealand
  2. 2.Dept of Computer ScienceUniversity of AucklandNew Zealand

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