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Improved Apriori Algorithm Based on Compressing Transactional Matrix Multiplication

机译：基于压缩事务矩阵乘法的改进Apriori算法

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摘要

Apriori algorithm is one of the most classical algorithm in association rules, however, the algorithm is low efficiency, such as firstly it needs to repeatedly scan the database, which spends much in I/O. Secondly, it create a large number of 2- candidate itemsets during outputting frequent 2-itemsets. Thirdly, it doesn't cancel the useless itemsets during outputting frequent k- itemsets. In the paper, it describes an improved algorithm based on the compressed matrices which improve the efficiency during creating frequent k- itemsets on three aspects, which simply scans the database once, after compressed transactional matrix, and by multiplied matrix get the frequent item sets, which effectively improved the efficiency in mining association rules.
机译：Apriori算法是关联规则中最经典的算法之一，但是该算法效率较低，例如首先需要重复扫描数据库，这在I / O上花费很多。其次，它在输出频繁的2个项目集的过程中创建了大量2个候选项目集。第三，它不会在输出频繁的k项目集时取消无用的项目集。在本文中，它描述了一种基于压缩矩阵的改进算法，该算法从三个方面提高了创建频繁k项集的效率，该方法只需对数据库进行一次扫描，然后压缩事务矩阵，再乘以矩阵即可得到频繁项集，有效提高了挖掘关联规则的效率。

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