Research of an improved Apriori algorithm in mining association rules
关联规则挖掘中对Apriori算法的一种改进研究

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.