挖掘数据流任意滑动时间窗口内频繁模式

Mining the Frequent Patterns in an Arbitrary Sliding Window over Online Data Streams

由于数据流的流动性与连续性,数据流所蕴含的知识会随着时间的推移而发生变化.因此,在绝大多数数据流的应用中,用户往往对新产生的流数据所包含的知识要比对历史流数据所包含的知识感兴趣得多.提出了一种挖掘数据流任意大小滑动时间窗口内频繁模式的方法MSW(mining sliding window).当数据流流过时,该方法使用滑动窗口树SW-tree在单遍扫描流数据的条件下及时捕获数据流上最新的模式信息.同时,该方法还周期性地删除滑动窗口树上过期的及不频繁的模式分支,从而降低滑动窗口树的空间复杂度与维护代价.此外,该方法还应用时间衰减模型逐步降低历史事务模式支持数的权重,并由此来区分最近产生事务与历史事务的模式.大量仿真实验的结果表明,算法MSS具有较高的效率与优良的可扩展性,同时也优于其他同类算法.

Because of the fluidity and continuity of data stream, the knowledge embedded in stream data is most likely to be changed as time goes by. Thus, in most data stream applications, people are more interested in the information of the recent transactions than that of the old. This paper proposes a method for mining the frequent patterns in an arbitrary sliding window of data streams. As data stream flows, the contents of the data stream are captured with a compact prefix-tree by scanning the stream only once. And the obsolete and infrequent items are deleted by periodically pruning the tree. To differentiate the patterns of recently generated transactions from those of historic transactions, a time decaying model is also applied. Extensive simulations are conducted and the experimental results show that the proposed method is efficient and scalable, and also superior to other analogous algorithms.