An Empirical Study of Qualities of Association Rules from a Statistical View Point


Maryann Dorn, Wen-Chi Hou, Dunren Che, Zhewei Jiang, Journal of Information Processing Systems Vol. 4, No. 1, pp. 27-32, Mar. 2008  

https://doi.org/10.3745/JIPS.2008.4.1.027
Keywords: Data Mining, Association Rule Mining, Rule Evaluation, Chi-square Test
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Abstract

Minimum support and confidence have been used as criteria for generating association rules in all association rule mining algorithms. These criteria have their natural appeals, such as simplicity; few researchers have suspected the quality of generated rules. In this paper, we examine the rules from a more rigorous point of view by conducting statistical tests. Specifically, we use contingency tables and chi-square test to analyze the data. Experimental results show that one third of the association rules derived based on the support and confidence criteria are not significant, that is, the antecedent and consequent of the rules are not correlated. It indicates that minimum support and minimum confidence do not provide adequate discovery of meaningful associations. The chi-square test can be considered as an enhancement or an alternative solution.


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Cite this article
[APA Style]
Dorn, M., Hou, W., Che, D., & Jiang, Z. (2008). An Empirical Study of Qualities of Association Rules from a Statistical View Point. Journal of Information Processing Systems, 4(1), 27-32. DOI: 10.3745/JIPS.2008.4.1.027.

[IEEE Style]
M. Dorn, W. Hou, D. Che, Z. Jiang, "An Empirical Study of Qualities of Association Rules from a Statistical View Point," Journal of Information Processing Systems, vol. 4, no. 1, pp. 27-32, 2008. DOI: 10.3745/JIPS.2008.4.1.027.

[ACM Style]
Maryann Dorn, Wen-Chi Hou, Dunren Che, and Zhewei Jiang. 2008. An Empirical Study of Qualities of Association Rules from a Statistical View Point. Journal of Information Processing Systems, 4, 1, (2008), 27-32. DOI: 10.3745/JIPS.2008.4.1.027.