基于频繁2项集支持矩阵的Apriori改进算法

捕要：Apriori算法在关联规则挖掘过程中需要多次扫描事务数据库，产生大量候选项目集，导致计算量过大。为解决该问题，提出一种基于频繁2项集支持矩阵的Apriori改进算法，通过分析频繁k＋1项集的生成机制，将支持矩阵与频繁2项集矩阵相结合实现快速剪枝，并大幅减少频繁k项集验证的计算量。实验结果表明，与Apriori算法和ABTM算法相比，改进算法明显提高了频繁项集的挖掘效率。

Improved Apriori Algorithm Based on Frequency 2-item Set Support Matrix

As Apriori algorithm used for mining association rules can lead to a large number of candidate itemsets and huge computations, an improved Apriori algorithm based on frequency 2-item set support matrix is proposed. By analyzing the generation mechanism of frequent k＋l item sets, the improved algorithm combines assistant matrix and frequent 2-item matrix to realize rapid puming, it can trim infrequent item set quickly and reduce the amount of calculation of k frequent item set verification. Experimental result shows that frequent itemsets mining efficiency of improved algorithm increases significantly compared with Apriori algorithm and ABTM algorithm.