关联规则挖掘中对Apriori算法的一种改进研究

针对Apriori算法寻找频繁项集问题，通过对事务数据库的布尔化表示，提出了一种直接利用布尔矩阵的行向量去搜寻频繁项集的思想。即通过向量的内积运算和判别准则逐步浓缩布尔矩阵的行向量，从而快速、直观地归纳出事务数据库的频繁项集。研究和分析表明，该方法不仅算法简单、只需扫描一次数据库，而且还具有搜索速度快、节省内存空间和处理项目集维数大等优点。对于处理超大型事务数据库和分布式事务数据库，同样也有较好的应用。

Research of an improved Apriori algorithm in mining association rules

An enhanced Apriori algorithm which directly used the row vectors of boolean matrix for transaction databases to find out the frequent item sets was presented in this paper. It used the inner product and discriminant rule to concentrate the row vectors of boolean matrix step by step, so the frequent item sets of transaction databases can be inducted quickly and intuitively. Studies and analysis of the proposed algorithm show that it can not only scan the database once, but also has the virtues in high speed, less memory cost and handling with large item set dimensions. It can also be well applied to super transaction database and distributed transaction database.