SMARViz: Soft Maximal Association Rules Visualization

Authors:
Tutut Herawan;Iwan Tri Yanto;Mustafa Mat Deris
Affiliations:
CIRNOV, Universitas Ahmad Dahlan, Yogyakarta, Indonesia and FTMM, Universiti Tun Hussein Onn Malaysia, Malaysia;CIRNOV, Universitas Ahmad Dahlan, Yogyakarta, Indonesia and FTMM, Universiti Tun Hussein Onn Malaysia, Malaysia;FTMM, Universiti Tun Hussein Onn Malaysia, Malaysia
Venue:
IVIC '09 Proceedings of the 1st International Visual Informatics Conference on Visual Informatics: Bridging Research and Practice
Year:
2009

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Abstract

Maximal association rule is one of the popular data mining techniques. However, no current research has found that allow for the visualization of the captured maximal rules. In this paper, SMARViz (Soft Maximal Association Rules Visualization ), an approach for visualizing soft maximal association rules is proposed. The proposed approach contains four main steps, including discovering, visualizing maximal supported sets, capturing and finally visualizing the maximal rules under soft set theory.