Abstract: Mining closed frequent itemsets from data streams has been extensively studied, in which NewMoment is re- garded as a typical algorithm. However, there is the problem of its big search space causing bad performance in time in NewMoment, This paper presented an algorithm called A-NewMoment which ameliorates NewMoment to mine closed frequent itemsets. Firstly, it designed a combinative data structure which uses an effective bit victor to represent items and an extended frequent item list to record the current closed frequent information in streams. Secondly, the new pru- ning strategics called WSS and CSS were proposed to avoid a large number of intermediate results generated, so the search space is reduced greatly. Finally, the pruning strategy called DNFIPS was also proposed to delete no closed fre- qucnt itemsets from HTC. At the same time, it also designed a novel strategy called DHSS to efficiently and dynamically maintain these operations that all closed frequent itemsedts are added and deleted. Theoretical analysis and experimental results show that the proposed method is efficient.

Key words: Data mining, Data strcams,Frcqucnt itcmscts,Closcd frequent item