项约束先过滤的最大频繁项集挖掘算法

在稠密型数据库中，现有最大频繁项集挖掘算法效率低、耗时长，挖掘结果模糊，不利于用户使用。为此，提出一种项约束先过滤的最大频繁项集挖掘算法——VCM。利用项包含约束过滤数据库，使用垂直数据表示数据集，采用深度优先的挖掘策略对数据库进行最大频繁相集的挖掘。实验结果表明，该算法快速有效，尤其在挖掘具有长模式的稠密数据库时优势明显。

Mining Algorithm of Maximal Frequent Itemset with Item Constraint Filtering First

In the dense database, mining maximal frequent itemsets takes too much time, and the results are too large to satisfy the users. This paper proposes a maximal frequent itemsets mining algorithm, called VCM. It filters the database with the constraints, uses the vertical data representation of data sets and adopts depth-first strategy for mining maximum frequent itemsets. Compared with other algorithms, experimental results show that the VCM algorithm is faster and more effective, and the advantage is remarkable when the databases are dense and with long patterns.