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-X2, that exploit the lift metric and the X2 metric respectively. We compare K2-Lift, K2-X2 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-X2 with a genetic algorithm approach on two benchmark networks show superior results on one network and comparable results on the other.

Exploiting Association and Correlation Rules Parameters for Learning Bayesian Networks

STORARI, Sergio;RIGUZZI, Fabrizio;LAMMA, Evelina
2009

Abstract

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-X2, that exploit the lift metric and the X2 metric respectively. We compare K2-Lift, K2-X2 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-X2 with a genetic algorithm approach on two benchmark networks show superior results on one network and comparable results on the other.
Storari, Sergio; Riguzzi, Fabrizio; Lamma, Evelina
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/534311
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 3
social impact