Concerning the infinite input and dynamic change in data stream environment, a new algorithm for detecting data stream outliers based on distance was proposed.
针对数据流的无限输入和动态变化等特点，提出一种新的基于距离的数据流离群点挖掘算法。
This paper proposed a new prediction method for outliers over data stream based on sparse representation to improve the optimum prediction speed and performance of outliers over data stream.
为了提高数据流中异常数据的预测速度与精度，提出一种基于稀疏表示的数据流异常数据预测方法。
The traditional algorithm of mining outliers cannot mine outliers in data stream effectively.
传统的离群点挖掘算法无法有效挖掘数据流中的离群点。
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