Efficient maintenance and mining of frequent itemsets over Online data streams with a sliding window

Online mining of streaming data is one of the most important issues in data mining. In this paper, we proposed an efficient one-pass algorithm, called MFI-TransSW ((M) under bar Mining (F) under bar requent (I) under bar temsets over a (T) under bar ransaction-sensitive (S) under bar liding (W) under bar indow), to mine the set of all 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. The experiments show that the proposed algorithm not only attain highly accurate mining results, but also run significant faster and consume less memory than existing algorithms for mining frequent itemsets over recent data streams.