CINECA IRIS Institutional Research Information System
IRIS è la soluzione IT che facilita la raccolta e la gestione dei dati relativi alle attività e ai prodotti della ricerca. Fornisce a ricercatori, amministratori e valutatori gli strumenti per monitorare i risultati della ricerca, aumentarne la visibilità e allocare in modo efficace le risorse disponibili.
EnglishIn 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.
Scheda prodotto non validato
Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo
| Data di pubblicazione: | 2009 | |
| Titolo: | Exploiting Association and Correlation Rules Parameters for Learning Bayesian Networks | |
| Autori: | S. Storari; F. Riguzzi; E. Lamma | |
| Rivista: | INTELLIGENT DATA ANALYSIS | |
| Parole Chiave: | Bayesian Networks Learning; K2; Association Rules; Correlation Rules | |
| 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. | |
| Digital Object Identifier (DOI): | 10.3233/IDA-2009-0388 | |
| Handle: | http://hdl.handle.net/11392/534311 | |
| Appare nelle tipologie: | 03.1 Articolo su rivista |
File in questo prodotto:
http://hdl.handle.net/11392/534311
Copyright © 2022