A Bayesian Association Rule Mining Algorithm
David Tian, Ann Gledson, Athos Antoniades, Aristos Aristodimou, Ntalaperas Dimitrios, Ratnesh Sahay, Jianxin Pan, Stavros Michael Stivaros, Goran Nenadić, Xiao‐Jun Zeng, John A Keane · 2013
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.