An Obstruction-Check Approach to Mining Closed Sequential Patterns in Data Streams

Online mining sequential patterns over data streams is an important problem in data mining. There are many applications of using sequential patterns in data streams, such as market analysis, network security and web tracking. For solving the problem of data mining in the time-based sliding window, Chang et al. proposed an algorithm called SeqStream. The SeqStream algorithm still scans the sliding window many times when IST(Inverse Closed Sequence Tree) needs to be updated. In this paper, we propose an obstruction-check approach to maintain the result of closed sequential patterns. Our approach is designed based on the lattice structure. Based on the lattice structure, we propose the EULB (Exact Update based on Lattice structure with Bit stream)-Lattice algorithm that is an exact method for mining data streams. We record additional information, instead of scanning the entire sliding window. The simulation results show that the proposed algorithm outperforms the SeqStream algorithm.