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An Interactive Visualization System for Mining Association Rules

  • Jianchao Han
  • Nick Cercone
  • Xiaohua Hu
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 95)

Abstract

We introduce an interactive visualization system, AViz, which discovers 3D numerical association rules from large data sets. The process of discovering association rules is visualized, which consists of six steps: preparing the raw data set, visualizing the original data set, cleaning the data, discretizing numerical attributes, and mining and visualizing the discovered association rules. The architecture of the AViz system is presented and each step is discussed. To discretize numerical attributes, three approaches, including equal-sized, bin-packing based equal-depth, and interaction-based approaches, are implemented and compared. The algorithm for mining and visualizing numerical association rules is proposed. Our experimental result on a census data set shows that the AViz system is useful and helpful for discovering and visualizing numerical association rules.

Keywords

Association Rule Mining Association Rule Support Threshold Nominal Attribute Confidence 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

  • Jianchao Han
    • 1
  • Nick Cercone
    • 1
  • Xiaohua Hu
    • 2
  1. 1.Department of Computer ScienceUniversity of Waterloo WaterlooWaterlooCanada
  2. 2.Knowledge Stream PartnerBostonUSA

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