A functional-dependencies-based Bayesian networks learning method and its application in a mobile commerce system.
This paper presents a new method for learning Bayesian networks from functional dependencies (FD) and third normal form (3NF) tables in relational databases. The method sets up a linkage between the theory of relational databases and probabilistic reasoning models, which is interesting and useful especially when data are incomplete and inaccurate. The effectiveness and practicability of the proposed method is demonstrated by its implementation in a mobile commerce system.
Liao
Stephen Shaoyi
SS
Department of Information Systems, Kowloon, Hong Kong, China. issliao@cityu.edu.hk
Wang
Huai Qing
HQ
Li
Qiu Dan
QD
Liu
Wei Yi
WY
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003132:Commerce; D003198:Computer Simulation; D003661:Decision Support Techniques; D019649:Food Industry / Q000191:economics* / Q000379:methods*; D018803:Models, Economic; D015233:Models, Statistical; D009038:Motion; D010363:Pattern Recognition, Automated / Q000379:methods*