Evelina Lamma, Fabrizio Riguzzi, Sergio Storari
A Bayesian network is an appropriate tool to work with a sort of uncertainty and probability, that are typical of real-life applications. Bayesian network arcs represent statistical dependence between different variables. In the data mining field, association rules can be interpreted as well as expressing statistical dependence relations. K2 is a well-known algorithm which is able to learn Bayesian network. In this paper we present two extensions of K2 called K2-Lift and K2-X2 that exploit two parameters normally defined in relation to association and correlation rules for learning Bayesian networks. The experiments performed show that K2-Lift and K2-X2 improve K2 with respect to both the quality of the learned network and the execution time.
Keywords: Probabilistic Reasoning, Bayesian Learning, Machine Learning, Data Mining, Bayesian Networks
Citation: Evelina Lamma, Fabrizio Riguzzi, Sergio Storari: Exploiting Association and Correlation Rules Parameterns for Improving the K2 Algorithm. In R.López de Mántaras and L.Saitta (eds.): ECAI2004, Proceedings of the 16th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2004, pp.500-504.