【摘要】 在数据流中挖掘频繁项集得到了广泛的研究,传统的研究方法大多关注于在数据流中挖掘全部频繁项集.由于挖掘全部频繁项集存在数据和模式冗余问题,所以对算法的时间和空间效率都具有更大的挑战性.因此,近年来人们开始关注在数据流中挖掘频繁闭项集,其中一个典型的工作就是Moment算法.本文提出了一种数据流中频繁闭项集的近似挖掘算法A-Moment.它采用衰减窗口机制、近似计数估计方法和分布式更新信息策略来解决Moment算法中过度依赖于窗口和执行效率低等问题.实验表明,该算法在保证挖掘精度的前提下,可以比Moment获得更好的效率.更多还原

【Abstract】 Mining frequent itemsets from data streams has extensively been studied,and most of them focus on finding complete set of frequent itemsets in a data stream.Because of numerous redundant data and patterns in main memory,they cannot get very good performance in time and space.Therefore,mining frequent closed itemsets in data streams becomes a new important problem in recent years,where algorithm Moment was regarded as a typical method of them.This paper presents an algorithm,called A-Moment,which uses the damped window technique,approximate count method and distributed updating strategy to get higher mining efficiency.Experimental results show that our algorithm performs much better than the previous approaches.更多还原