Exploiting Association and Correlation Rules - Parameters for Improving the K2 Algorithm
نویسندگان
چکیده
A Bayesian network is an appropriate tool to deal with the uncertainty that is typical of real-life applications. Bayesian network arcs represent statistical dependence between different variables. In the data mining field, association and correlation rules can be interpreted as well as expressing statistical dependence relations. K2 is a well-known algorithm which is able to learn Bayesian networks. In this paper we present two extensions of K2 called K2-Lift and K2-X that exploit two parameters normally defined in relation to association and correlation rules. The experiments performed show that K2-Lift and K2-X improve K2 with respect to both the quality of the learned network and the execution time.
منابع مشابه
Exploiting association and correlation rules parameters for learning Bayesian networks
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