This paper proposes a novel online streaming data anomaly detection method. By using the new method, the improved L 1 detection neighbor region optimizes the initial hyper-grid-based anomaly detection method by decreasing the quantity of neighbor detection region, and online ensem-
ble learning adapts to the distribution evolving characteristic of streaming data and overcomes the difficulty of obtaining the optimal hyper-grid structure. To validate the proposed method, the paper uses a real-world dataset and two simu- lated datasets and finds out that the experimental results are
near to the optimal results.

EB00000002448K			Available
EB00000002449K			Available