Mining frequent patterns in an arbitrary sliding window over data streams

This paper proposes a method for mining the frequent patterns in an arbitrary sliding window of data streams. As streams flow, the contents of which are captured with SWP-tree by scanning the stream only once, and the obsolete and infrequent patterns are deleted by periodically pruning the tree. To differentiate the patterns of recently generated transactions from those of historic transactions, a time decaying model is also applied. The experimental results show that the proposed method is efficient and scalable, and it is superior to other analogous algorithms.