Outlier Detection
Outlier Detection
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DOI：
10.1007/978-0-387-09823-4_7
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发表时间：
2010-01-01
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影响因子：
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通讯作者：
Ben-Gal, Irad
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文献类型：
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作者：
Ben-Gal, Irad
离群点检测是许多数据挖掘应用中的主要步骤。我们提出了几种离群值检测方法，同时区分单变量与多变量技术和参数与非参数程序。在存在离群值的情况下，应特别注意确保所用估计量的稳健性。数据挖掘中的离群点检测通常基于距离度量、聚类和空间方法。
Outlier detection is a primary step in many data-mining applications. We present several methods for outlier detection, while distinguishing between univariate vs. multivariate techniques and parametric vs. nonparametric procedures. In presence of outliers, special attention should be taken to assure the robustness of the used estimators. Outlier detection for Data Mining is often based on distance measures, clustering and spatial methods.