基于约束网络的因果关联规则挖掘研究

因果关联规则是知识库中一类特殊且重要的知识类型,相对一般关联规则,其优势在于能够揭示深层知识。首先对因果关系的特征和因果关联规则的挖掘方法进行了简介。针对在挖掘初始阶段如何限定可能导致结果的原因变量集合这一问题,运用了约束网络原理来构建一个实际系统变量间的因果关系结构。通过该因果关系结构可以比较容易地导出原因变量集合及各变量的类型,从而降低挖掘的复杂性,为提高挖掘结果的准确性提供有利条件。约束网络的引入优化了因果关联规则的挖掘过程,使之趋于更完备。

Research on Causal Association Rule Mining Based on Constraint Network

Causal association rule is one kind of important and special knowledge type in knowledge base,which can reveal the deep knowledge relative to general association rule.Characteristics of the causal relation and the causal association rule mining were briefly introduced at first.Based on that,constraint network theory was used to construct a causal relation structure of variables in practical system so as to solve the problem of how to restrict the casual variable set in initial step of the mining.Casual variable set as well as types of the variables can be inferred easily from the causal relation structure,so complexity of the mining would be reduced and the results would be more accurate.Introduction of the constraint network can make the process of causal association rule mining become more complete.