title: A functional-dependencies-based Bayesian networks learning method and its application in a mobile commerce system.
abstract: 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.
authors.affiliation: Department of Information Systems, Kowloon, Hong Kong, China. issliao@cityu.edu.hk
authors.forename: Stephen Shaoyi
authors.initials: SS
authors.lastname: Liao
authors.forename: Huai Qing
authors.initials: HQ
authors.lastname: Wang
authors.forename: Qiu Dan
authors.initials: QD
authors.lastname: Li
authors.forename: Wei Yi
authors.initials: WY
authors.lastname: Liu
mesh_terms: 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*