A Bayesian Association Rule Mining Algorithm

This paper proposes a Bayesian association rule mining algorithm (BAR) which combines the Apriori association rule mining algorithm with Bayesian networks. Two interesting-ness measures of association rules: Bayesian confidence (BC) and Bayesian lift (BL) which measure conditional dependence and ind...

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Bibliographic Details
Main Authors: Tian, David, Gledson, Ann, Antoniades, Athos, Aristodimou, Aristo, Dimitrios, Ntalaperas, Sahay, Ratnesh, Jianxin Pan, Stivaros, Stavros, Nenadic, Goran, Xiao-jun Zeng, Keane, John
Format: Conference Proceeding
Language:English
Subjects:
Association rules
Bayes methods
Bayesian association rules
Bayesian confidence
Bayesian lift
Bayesian networks
Diseases
Educational institutions
Itemsets
joint probability distribution
Joints
Probability distribution
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Summary:This paper proposes a Bayesian association rule mining algorithm (BAR) which combines the Apriori association rule mining algorithm with Bayesian networks. Two interesting-ness measures of association rules: Bayesian confidence (BC) and Bayesian lift (BL) which measure conditional dependence and independence relationships between items are defined based on the joint probabilities represented by the Bayesian networks of association rules. BAR outputs best rules according to BC and BL. BAR is evaluated for its performance using two anonymized clinical phenotype datasets from the UCI Repository: Thyroid disease and Diabetes. The results show that BAR is capable of finding the best rules which have the highest BC, BL and very high support, confidence and lift.
ISSN:1062-922X
2577-1655
DOI:10.1109/SMC.2013.555