一种扩展的关联规则挖掘算法  

Mining Algorithm of Extended Association Rule

作　　者：胡陈勇[1] 刘大有[1] 刘亚波[1] 

机构地区：[1]吉林大学计算机科学与技术学院

出　　处：《吉林大学学报（理学版）》2005年第2期153-156,共4页Journal of Jilin University:Science Edition

基　　金：国家自然科学基金(批准号:60173006);国家高技术研究发展计划项目基金(批准号:2003AA118020);吉林大学符号计算与知识工程教育部重点实验室项目基金

摘　　要：提出一种扩展的关联规则挖掘算法,该算法扩展了传统算法都是针对二元数据矩阵的缺点,引入了挖掘量化的关联规则,通过试验发现,该算法同样适用于传统的布尔矩阵.该算法主要是基于主成分分析法发现数据中特征向量的思想来挖掘数据中的量化关联,同时定义了比例项目集.该算法在时空复杂性上也取得了较好的效果.In this paper is proposed a new method for mining quantitative association rules by means of principal component analysis. In contrast to traditional Boolean data matrix, our algorithm is based on quantitative database, which contains some value knowledge for us. The experiment illustrated that the algorithm also works well on canal binary data matrices. Based on the idea of principal component analysis (PCA) discovering the principal component as quantitative association, the ratio item set is definited as well. In additional, it is shown that for the treatment of the complexity of time and space the method is more effective than traditional methods.

关 键 词：关联规则 主成分分析 奇异值分解 比例项目集 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]