提出了一种基于树的频繁模式挖掘算法TBA-FP。它以树表示法压缩数据库所含模式信息，将挖掘问题转化为按深度优先策略构造频繁模式树，并引入了虚拟裁剪等优化技术。实验表明，TBA-FP挖掘“长”模式的时间效率与空间可伸缩性远远优于经典算法Apriori.

A tree-based algorithm for mining of frequent patterns, TBA-FP, is proposed in this paper. The term, tree-based, has two meanings. First, a tree-based representation is used to compress the pattern information in the database. Second, the mining process is regarded as a process of constructing a frequent pattern tree in depth first order. Optimizing techniques, such as pseudo clipping, is discussed. Experiments show that TBA-FP is much more efficient and scalable than the classical algorithm, Apriori.