一种基于滑动窗口的数据流频繁项集挖掘算法

摘要

数据流的流动性与连续性,使得数据流所蕴含的知识会随着时间的推移而发生变化.挖掘数据流中的频繁项集是一项意义重大且具有挑战性的工作.提出一种基于滑动窗口数据流的频繁项集挖掘——FIUT-Stream算法,FIUT-Stream算法分块挖掘数据流,在内存中维持一个滑动窗口数据的概要结构,随着窗口滑动动态更新该存储结构,利用FIUT算法进行频繁项集挖掘.实验表明,该算法能节省内存空间、精确获得频繁项集.%The flowability and continuity of data stream make the knowledge implicated in data streams change as the time passes. To mine frequent itemsets in data streams is a significant and challenging work. A new algorithm of FIUT-Stream, mining the frequent itemsets in data streams over sliding window, is proposed in the article. FIUT-Stream mines the data stream by blocks and maintains in memory an outlined structure of a sliding window data, dynamically updates the storage structure when the window slides, and uses FIUT algorithm to mine the frequent itemsets. Experiments show that this algorithm can save memory space and accurately acquires the frequent itemsets.