| 摘要: |
| 贝叶斯网络是用来表示变量集合的连续概率分布的图形模式,它提供了一种自然地表示因果信息的方法,用来发现数据间的潜在关系.贝叶斯网络的学习也就是要找出一个能够最真实反映现有数据库中各数据变量相互之间的依赖关系的贝叶斯网络模型,即根据数据样本D和先验知识ζ,找出后验概率p(sh|D,ζ)最大的贝叶斯网络S.该文在数学上对贝叶斯网络的学习方法进行了严格的推导,用一个实例来说明贝叶斯网络的计算过程,并介绍了贝叶斯网络在数据挖掘领域内的应用. |
| 关键词: 数据挖掘,贝叶斯网络,贝叶斯概率,先验概率,后验概率. |
| DOI： |
| 分类号: |
| 基金项目:本文研究得到国家CIMS工程研究中心基金(No.CIMS-JJ.96-001)资助. |
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| Bayesian Network for Data Mining |
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MU Chun-di,DAI Jian-bin,YE Jun
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| Abstract: |
| Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. It is a natural way to express the causal information, and to discover the hidden patterns among the data. Learning of Bayesian network is to find out a network model that best represents the dependent relationships of the variables in a database, that is, given sample D and prior knowledge ζ, to find a Bayesian network S that fits the maximum posterior probability p(sh|D,ζ). In this paper, the learning process of the network is strictly derived, and a case study is presented to indicate the applications of Bayesian network in data mining. |
| Key words: Data mining, Bayesian network, Bayesian probability, prior probability, posterior probability. |
