一种基于压缩矩阵的关联规则挖掘算法

A association rules mining algorithm base on compression matrix

针对在关联规则中的Apriori算法进行了深入研究的基础上,提出了一种基于压缩矩阵的关联规则挖掘算法(CMApriori算法)。该算法只需扫描一次数据库,在矩阵上采用事务压缩和项目压缩技术,节省了数据占用的内存空间。在对建立好的压缩矩阵上只需进行简单的计数运算即可得到频繁项集。仿真实验证明:该算法与Apriori算法相比,运算效率大大提高。

Based on fully analyzing the Apriori algorithm of an association rules mining algorithm, this paper present a new association rules mining algorithm based on compression matrix（CMApriori algorithm）.This algorithm scans the database only once and utilizes compress techniques , which reduces the memory space of data.It finds frequent itemsets through simple calculation on compression matrix.The result of the experiment shows that CMApriori algorithm with high efficiency than Apriori algorithm.