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.