Liao Stephen Shaoyi, Wang Huai Qing, Li Qiu Dan, Liu Wei Yi
Department of Information Systems, Kowloon, Hong Kong, China.
IEEE Trans Syst Man Cybern B Cybern. 2006 Jun;36(3):660-71. doi: 10.1109/tsmcb.2005.862492.
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.
本文提出了一种从关系数据库中的函数依赖（FD）和第三范式（3NF）表学习贝叶斯网络的新方法。该方法在关系数据库理论与概率推理模型之间建立了一种联系，这一点特别有趣且有用，尤其是在数据不完整和不准确的情况下。所提方法的有效性和实用性通过其在移动商务系统中的实现得到了证明。
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