摘要
数据挖掘技术是实现智能决策支持系统的一个重要手段 ,关联规则是数据挖掘的一个重要内容 .传统的 Apriori算法仅适用于挖掘数据间的定性关联关系 ,但数据间的定量关联关系对决策更有帮助 .属性值的离散映射是挖掘定量关联规则的一个重要环节 ,离散映射中属性值区间的划分粒度是影响数据挖掘质量的一个重要因素 .本文结合粗集理论提出了一个确定属性值划分粒度的方法 ,在此基础上设计出一个挖掘定量关联规则的算法 :Apriori 2 ,利用Apriori
Data mining is an important method of building intelligent decision support system, association rule is an important content of data mining. Apriori, the traditional algorithm, can only discovery the qualitative associated relation among data, but the quantitative associated relation is more helpful in decision making. Mapping attribute's value into discrete characters is a key step in mining quantitative association rules, in which the partition granularity of attribute's value is a key factor affecting the quality of the result of data mining. In this paper, by integrating the theory of rough sets,a method of mapping attribute's value into discrete character with a fine value partition granularity is developed, and then a new algorithm of mining quantitative association rules, Apriori2, is presented. Many rules which are helpful in decision making can be mined by Apriori2.
出处
《管理科学学报》
CSSCI
2001年第4期41-48,共8页
Journal of Management Sciences in China
基金
国家自然科学基金资助项目 (70 0 710 0 8)
