为解决传统频繁模式挖掘算法效率不高的问题，提出了一种改进的基于FP-tree （Frequent pattern tree）的Apriori频繁模式挖掘算法.首先，在Apriori算法的连接步加入连接预处理过程；其次，对CP-tree （Compact Pattern tree）进行扩展，构造了一个新的树结构ECP-tree （Extension of Compact Pattern tree），新的树结构只需对数据库进行一次扫描就能构造出一棵紧凑的前缀树，且支持交互式挖掘与增量挖掘；然后，将改进点与APFT算法结合，用于挖掘频繁模式；最后，使用UCI数据库中两个数据集进行实验.实验结果表明：改进算法具有较高的挖掘效率，频繁模式挖掘速度显著提升.
In order to solve the problem that the low efficiency of traditional frequent patterns mining algorithm, an improved Apriori algorithm based on FP-tree is proposed. Firstly, the join preprocessing process is added to the join step of Apriori algorithm. Secondly, the CP-tree is extended to construct a new tree structure, ECP-tree. The new tree structure can construct a compact prefix tree with only one scan of the database, and support interactive mining and incremental mining. Then, the improved points are combined with the APFT algorithm for mining frequent patterns. Finally, experiments are performed using two datasets in the UCI database. The experimental results show that the improved algorithm has higher mining efficiency and the frequent pattern mining speed is significantly improved.