In order to find local outliers in data stream, this paper analyzes traditional outliers detection algorithms, and introduces an outlier detection algorithm based on Wavelet Density Estimation(WDE). It uses a multi-scale and multi-granularity characteristics of WDE using the wavelet probability threshold to judge the data stream within the current sliding window data points as outliers, and discusses outlier detection in data stream process. Simulation results show that this algorithm has higher detection efficiency and accuracy in the data stream than Kernel Density Estimation(KDE) algorithm.%为能及时发现数据流上的局部离群点,分析数据流已有的离群点挖掘算法,提出基于小波密度估计的离群点检测算法.利用小波密度估计多尺度和多粒度的特点,通过小波概率阈值判断数据流中当前滑动窗口内的数据点是否为离群点,并对数据流中离群点检测过程进行讨论.仿真结果表明,与核密度估计算法相比,该算法的检测效率与精度较高.
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