摘要： 众多研究者致力于将朴素贝叶斯方法与原有的ILP系统结合，形成各种各样的多关系朴素贝叶斯分类器(MRNBC)。该文提出形成朴素贝叶斯分类器的一阶扩展的一般方法。现实中关系数据库广泛存在，可以直接作用于数据库表，而无须转换表示形式的MRNBC则是研究的重点，该方法主要基于关系数据库理论，分析了进行一阶扩展的关键问题。

关键词: 多关系数据挖掘, 朴素贝叶斯, 分类, 归纳逻辑程序设计, 关系数据库

Abstract: Many researchers focus on the combination of ILP system together with the naive Bayesian classifier. And various Multi-Relational Naïve Bayesian Classifiers(MRNBC) have been proposed. This paper describes a methodology based on ILP for upgrading naïve Bayesian classifiers to first-order logic. There are almost relational databases everywhere in real-life, so this paper also aims at solving how to shape MRNBC based on relational databases theory. The upgrading can deal with data in multiple tables directly and transformation is not needed.

Key words: Multi-Relational Data Mining(MRDM), Naï, ve Bayesian, classification, Inductive Logic Programming(ILP), relational database

中图分类号: 

• TP311