基于关联规则的分类规则约简方法  

Classification Rule Reduction Method Based on Association Rule

作　　者：王琦[1] 李霞[2] 

机构地区：[1]运城学院计算机科学与技术系,山西运城044000 [2]运城学院公共计算机教学部,山西运城044000

出　　处：《计算机工程》2012年第9期46-48,共3页Computer Engineering

基　　金：山西省高等学校科技研究开发基金资助项目(20091150);运城学院基金资助项目(JC-2009015)

摘　　要：分析分类规则内属性之间的相关性,提出一种分类规则约简方法。针对原始训练集构造FP树,获取相应的关联规则集,对关联规则后件属性(集),采用置信度α描述该属性(集)相对于其所在分类规则的重要程度。在分类规则集中,约简α值小于阈值的属性,从而约简分类规则长度。利用UCI机器学习及SDSS DR7数据进行实验,结果表明该方法具有较高的分类效率。This paper proposes a classification rule reduction method by analyzing the correlation of attributes in classification rules. It obtains the association rule set by analyzing the correlation among the attributes of training set, describes the importance degree in the classification rule by using the degree of confidence a of the association rule. The later part of the association rule, whose α is larger than threshold value η, is deleted in the classification rule. Experimental results validate that this method has higher classification effectiveness by using UCI and SDSS data as the decision system.

关 键 词：数据挖掘 分类规则 关联规则 UCI数据 SDSS DR7数据 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]