A mining algorithm for frequent closed pattern on data stream based on sub-structure compressed in prefix-tree

The knowledge embedded in data stream has the property of decaying with time, so people are more interested in the new data stream. This paper proposes a method for mining the frequent closed patterns in a sliding window to capture information timely and accurately when new data stream arrives. Data stream is divided into several basic windows. All possible frequent closed patterns are mined in each basic window and be stored in Closed Pattern-tree in the form of node compression to save space; As the data in sliding window updates, Closed Pattern-tree can be incrementally updated and the infrequent or unclosed patterns will be deleted from the tree. The experimental results of the simulation show that the method for mining the frequent closed patterns in a sliding window is efficient in time and space.