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.
EnglishA Bayesian network is an appropriate tool to deal with the uncertainty that is typical of real-life applications. Bayesian network arcs represent statistical dependence between different variables. In the data mining field, association and correlation 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
Scheda prodotto non validato
Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo
| Titolo: | Exploiting Association and Correlation Rules Parameters for Improving the K2 Algorithm |
| Autori: | |
| Data di pubblicazione: | 2004 |
| Serie: | |
| Abstract: | A Bayesian network is an appropriate tool to deal with the uncertainty that is typical of real-life applications. Bayesian network arcs represent statistical dependence between different variables. In the data mining field, association and correlation 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 |
| Handle: | http://hdl.handle.net/11392/1195332 |
| ISBN: | 9781586034528 1586034529 |
| Appare nelle tipologie: | 04.2 Contributi in atti di convegno (in Volume) |
File in questo prodotto:
Copyright © 2020