Exploiting association and correlation rules parameters for learning Bayesian networks

In data mining, association and correlation rules are inferred from data in order to highlight statistical dependencies among attributes. The metrics defined for evaluating these rules can be exploited to score relationships between attributes in Bayesian network learning. In this paper, we propose two novel methods for learning Bayesian networks from data that are based on the K2 learning algorithm and that improve it by exploiting parameters normally defined for association and correlation rules. In particular, we propose the algorithms K2-Lift and K2-$X^{2}$, that exploit the lift metric and the $X^2$ metric respectively. We compare K2-Lift, K2-$X^{2}$ with K2 on artificial data and on three test Bayesian networks. The experiments show that both our algorithms improve K2 with respect to the quality of the learned network. Moreover, a comparison of K2-Lift and K2-$X^{2}$ with a genetic algorithm approach on two benchmark networks show superior results on one network and comparable results on the other.