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 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.

[1]  Erol Sarigul,et al.  Exploring Bayesian networks for medical decision support in breast cancer detection , 2010 .

[2]  Constantinos S. Pattichis,et al.  The effects of applying cell-suppression and perturbation to aggregated genetic data , 2012, 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE).

[3]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[4]  Chun-Ho Wu,et al.  Using a fuzzy association rule mining approach to identify the financial data association , 2012, Expert Syst. Appl..

[5]  Qiang Shen,et al.  A rough-fuzzy approach for generating classification rules , 2002, Pattern Recognit..

[6]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[7]  Jesús Alcalá-Fdez,et al.  A multi-objective evolutionary algorithm for mining quantitative association rules , 2011, 2011 11th International Conference on Intelligent Systems Design and Applications.

[8]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[9]  Jian Pei,et al.  Data Mining: Concepts and Techniques, 3rd edition , 2006 .

[10]  Behrouz Minaei-Bidgoli,et al.  Mining numerical association rules via multi-objective genetic algorithms , 2013, Inf. Sci..

[11]  Carol Friedman,et al.  Mining multi-item drug adverse effect associations in spontaneous reporting systems , 2010, BMC Bioinformatics.

[12]  Reda Alhajj,et al.  Fuzzy association rule mining framework and its application to effective fuzzy associative classification , 2011, WIREs Data Mining Knowl. Discov..

[13]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[14]  Andrzej Skowron,et al.  Rough set methods in feature selection and recognition , 2003, Pattern Recognit. Lett..

[15]  Josiane Zerubia,et al.  Hyperparameter estimation for satellite image restoration using a MCMC maximum-likelihood method , 2002, Pattern Recognit..

[16]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .