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 want to present an extension of K2 called K2-rules that exploits a parameter normally defined in relation to association rules for learning Bayesian networks. The experiments performed show that K2-rules improves K2 with respect to both the quality of the learned network and the execution time

Improving the K2 Algorithm Using Association Rules Parameters

LAMMA, Evelina;RIGUZZI, Fabrizio;STORARI, Sergio
2004

Abstract

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 want to present an extension of K2 called K2-rules that exploits a parameter normally defined in relation to association rules for learning Bayesian networks. The experiments performed show that K2-rules improves K2 with respect to both the quality of the learned network and the execution time
2004
Bayesian Networks Learning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/1195331
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