摘要
从关系数据挖掘的角度提出了挖掘特异规则的方法,该方法通过面向属性的方法来识别特异数据.借鉴Chi2算法的思想实现了特异数据的离散,并定性地描述了数据的特异程度,结合经典的归纳逻辑程序设计系统FDIL,自然地挖掘出了特异规则,突破了传统命题级数据挖掘的框架.试验结果表明利用该方法能够发现被传统的关联规则挖掘算法所忽略的有价值的知识.
The authors, from the view of relational data mining (RDM), propose a new method to excavate peculiar rules, which recognizes the peculiar data based on attribute-oriented method, and uses Chiz algorithm for reference to discretize the peculiar data and qualitatively describe the peculiar degree of the data. The new method, adopting the classical inductive logic programming design system FOIL, naturally mines the peculiar rules, breaking through the frame of traditional prepositional data mining. The experiment results show that the method can discover and mine the valuable knowledge ignored by the traditional mining algorithm of association rules.
出处
《北京工业大学学报》
CAS
CSCD
北大核心
2003年第4期495-499,共5页
Journal of Beijing University of Technology
基金
国家自然科学基金资助项目(60173014)
北京市自然科学基金资助项目(4022003)
关键词
归纳逻辑程序设计
关系数据挖掘
特异规则
inductive logic programming
relational data mining
peculiar rules
