The existance of outlier always leads to inaccurate, even wrong results in data mining. The outlier detection algorithm now available should be improved including its versatility, effectiveness, user-friendliness, and the performance in processing high-dimensional and large databases. An effective and global outlier detection method is proposed. Agglomerative hierarchical clustering is performed, and the isolated degree of the data can be visually judged by the clustering tree and distance matrix, and the number of the outliers can be determined and the outliers are identified unsupervisedly from the top to down of the clustering tree. Experimental results show that the method can effectively detect the top-n global outliers, and applicable to datasets of various shapes'. Experimental results show that the algorithm is efficient, user-friendly, and applicable to detect the outliers for high-dimensional and large databases.%为了提高语音信号的信噪比,提出一种经验模态分解与自适应滤波相结合的语音增强法.对带噪语音进行经验模态分解,得到有限个固有模态函数,把所有的固有模态函数按顺序分成三组,将每一组所包含的固有模态函数叠加,得到三个子信号；对三个子信号进行自适应滤波,消除噪声；将降噪后的子信号重构得到增强后的语音.仿真实验表明,所提方法的语音增强效果优于自适应滤波.
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