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Online mining of closed frequent itemsets over streaming data is one of the most important issues in mining data streams. In this paper, we propose an efficient one-pass algorithm, NewMoment to maintain the set of closed frequent itemsets in data streams with a transaction-sensitive sliding window. An effective bit-sequence representation of items is used in the proposed algorithm to reduce the time and memory needed to slide the windows. Experiments show that the proposed algorithm not onlydoi:10.1016/j.eswa.2007.12.054 fatcat:3udpbwkwm5gzxja6xqchtyieee