摘要
数据挖掘的一个重要任务便是从数据库中挖掘出有趣的关联规则。传统的关联规则挖掘方法一般基于支持度—置信度体系,时常会挖掘出虚假规则或忽略掉有用的规则。针对这一问题,借鉴对照实验的思想,提出基于T统计量的关联规则挖掘方法,用显著度代替置信度,使挖掘出的规则更具有统计显著性。算例分析和数据实验表明,这种方法可以解决传统关联规则方法存在的上述问题,提高关联规则的有效性。
One major purpose of data mining is to discover interesting association rules.And traditional data mining methods are generally based on support-confidence system,which might frequently dig out false rules or neglect useful ones.At terms of this problem,referring to the mind of control experiment,this paper developed T statistical-based association rule mining methodology,in which used significance to replace confidence in order to obtain results with more statistical significance.Accounting case analysis and data mining results show that this new method can efficiently solve problems underlying in traditional association rules methods,and thus improve the validity of the rules.
出处
《计算机应用研究》
CSCD
北大核心
2011年第6期2073-2077,共5页
Application Research of Computers
基金
国家自然科学基金资助项目(70890080
70971033)
教育部新世纪优秀人才支持计划资助项目(NCET-08-0172)
关键词
数据挖掘
关联规则
T统计量
显著性
data mining
association rule
T statistical
significant
