In many applications, stream data are too voluminous to be collected in a central fashion and often transmitted on a distributed network. In this paper, we focus on the outlier detection over distributed data streams in real time, firstly, we formalize the problem of outlier detection using the kernel density estimation technique. Then, we adopt the fading strategy to keep pace with the transient and evolving natures of stream data, and mico-cluster technique to conquer the data partition and "one-pass" scan. Furthermore, our extensive experiments with synthetic and real data show that the proposed algorithm is efficient and effective compared with existing outlier detection algorithms, and more suitable for data streams.
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