一种直接生成跨层频繁模式的算法

Directly Generating Cross Level Frequent Patterns

定义了一种基于树的跨层模式信息表示 ,在此基础上提出了直接生成跨层频繁模式算法TBA CLFP。它能高效地挖掘多层特别是跨层频繁模式。实验表明TBA CLFP的时间效率与空间可伸缩性远优于Cumulate,Apriori等经典算法。TBA CLFP可进一步推广到数量型频繁模式挖掘。

In this paper, a novel algorithm, TBA CLFP, is proposed, which employs a tree based data structure to represent cross level pattern information and can directly mine single, multiple and cross level frequent patterns. Comparative experiments with Apriori and Cumulate show that TBA CLFP is much more efficient and scalable. TBA CLFP can be transplanted to mine quantitative cross level frequent patterns.