A Bayesian Association Rule Mining Algorithm

Author

Tian, D. ; Gledson, Ann ; Antoniades, Andreas ; Aristodimou, Aristo ; Dimitrios, Ntalaperas ; Sahay, Ratnesh ; Jianxin Pan ; Stivaros, Stavros ; Nenadic, Goran ; Xiao-Jun Zeng ; Keane, John

Author_Institution

Sch. of Comput. Sci., Univ. of Manchester, Manchester, UK

fYear

2013

fDate

13-16 Oct. 2013

Firstpage

3258

Lastpage

3264

Abstract

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.

Keywords

belief networks; data mining; probability; BAR; Bayesian association rule mining algorithm; Bayesian confidence; Bayesian lift; Bayesian networks; Thyroid disease; UCI repository; apriori association rule mining algorithm; clinical phenotype datasets; diabetes; interesting-ness measures; performance evaluation; Association rules; Bayes methods; Diseases; Educational institutions; Itemsets; Joints; Probability distribution; Bayesian association rules; Bayesian confidence; Bayesian lift; Bayesian networks; joint probability distribution;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on

Conference_Location

Manchester

Type

conf

DOI

10.1109/SMC.2013.555

Filename

6722308