Discovery of unapparent association rules based on extracted probability

Authors:
Chia-Wen Liao;Yeng-Horng Perng;Tsung-Lung Chiang
Affiliations:
Department of Civil Engineering, China University of Technology, Taipei, Taiwan;Department of Architecture, National Taiwan University of Science and Technology, Taipei, Taiwan;Department of Civil Engineering, National Taiwan University, Taipei, Taiwan
Venue:
Decision Support Systems
Year:
2009

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Abstract

Association rule mining is an important task in data mining. However, not all of the generated rules are interesting, and some unapparent rules may be ignored. We have introduced an ''extracted probability'' measure in this article. Using this measure, 3 models are presented to modify the confidence of rules. An efficient method based on the support-confidence framework is then developed to generate rules of interest. The adult dataset from the UCI machine learning repository and a database of occupational accidents are analyzed in this article. The analysis reveals that the proposed methods can effectively generate interesting rules from a variety of association rules.