An Algorithm to Approximately Mine Frequent Closed Itemsets from Data Streams
Mao Guo · Dianzi xuebao · 2007
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.