TWCT-Stream:数据流上的频繁模式挖掘算法

TWCT-Stream:Algorithm for mining frequent patterns in data streams

提出一种结合倾斜时间窗的TWCT树结构,可以保存不同时间粒度下频繁模式的完全集,并设计了其顺序更新和删除算法,使其能够存储在外存,从而有效地降低算法的内存空间需求。结合TWCT树结构特点,提出了数据流上的频繁模式挖掘算法TWCT-Stream,其模式生长的TWCT-Growth算法按字典顺序生成频繁模式,以配合TWCT结构的顺序更新。实验证实算法的内存需求低于FP-Stream等同类算法。

A TWCT tree structure is proposed with tilted-time window framework embedded,which can maintain the complete set of frequent patterns at multiple time granularities.And this paper designs the sequential update and delete algorithms for the structure,which makes it can be saved in auxiliary storage in order to reduce the algorithms’ requirements of the main memory effectively.Taking advantage of this characteristic,TWCT-Stream,a frequent pattern mining algorithm in data stream,is proposed.And its pattern growth algorithm,TWCT-Growth,generates frequent patterns in lexical order suitable for the sequential updating of TWCT structure.Experiments have proved its memory requirements lower than the same kind of algorithms like FP-Stream and so on.