摘要
关于关联规则挖掘和因果关系发现之间的关系,较为全面地分析比较结果目前尚不多见。本文在说明关联规则与因果规则各自特点的基础上,从方向性、对人类行为的指导意义以及如何将他们联系起来三个方面进行了理论上的分析比较。分析结果表明因果发现能够找出事物间的内在机制性联系,并且可以据此对关联规则进行推理和检验。最后,将两种数据挖掘方法应用于一个人口统计数据集,并比较了挖掘结果,从而进一步验证理论分析的结论。
Association rule data mining and causal discovery are two important data processing methods, but the comparison research between them is scarce now. Based on the description and analysis of the characteristics of association rules and causal rules, this work compares them theoretically in three aspects: directivity, guldance to the human behaviors, deduction and induction between them. The result shows that the intrinsic mechanic relationships between things can be obtained by the causal discovery, then we can predict the association rules based on it. Finally, this two kinds of data mining methods are applied to a real census income data set. The compared mining result validates the anterior analysis result.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2005年第3期328-333,共6页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金(No.60172037)
教育部"跨世纪优秀人才培养计划"基金
关键词
数据挖掘
关联规则
因果发现
Data Mining
Association Rule
Causal Discovery
