ECAI 2004 Conference Paper

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Exploiting Association and Correlation Rules Parameterns for Improving the K2 Algorithm

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.


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ECAI-2004 is organised by the European Coordinating Committee for Artificial Intelligence (ECCAI) and hosted by the Universitat Politècnica de València on behalf of Asociación Española de Inteligencia Artificial (AEPIA) and Associació Catalana d'Intel-ligència Artificial (ACIA).