基于Aproiri算法的频繁项集挖掘优化方法

为了进一步降低扫描数据库的次数和减轻内存负担，从而更好地提高挖掘频繁项集的效率，一种基于Apriori的优化算法（M-Apriori）被提出. 该方法通过构建频繁状态矩阵来存放项集的频繁状态，构建事务布尔矩阵来存放事务与项集的关系，此算法只需在初始化阶段扫描一次数据库产生初始的频繁状态矩阵和事务布尔矩阵，并在此基础上直接递推产生所有的频繁项集. 实验证明，与Apriori算法相比，M-Apriori算法具有更好的性能与效率.

Frequent Itemsets Mining Optimization Methods Based on Aproiri Algorithm

To reduce the number of database scanning and reduce the burden of memory further, also to improve the efficiency of mining frequent itemsets better, an Apriori-based optimization algorithm (M-Apriori) is proposed. The method stores frequent itemsets state by constructing the frequent state matrix and store the relationship between the transaction and itemsets by constructing the Boolean matrix. The algorithm scans the database only once and generates the initial frequent state matrix and the Boolean matrix during the initialization phase. On this basis, all frequent itemsets can be found directly without scanning the database repeatedly. Experiments show that M-Apriori algorithm has better performance and efficiency compared with the Apriori algorithm.