Detection of outliers

被引：94

作者：

Hadi, Ali S. ^{[1
,2
]}

Imon, A. H. M. Rahmatullah ^{[3
]}

Werner, Mark ^{[1
]}

机构：

[1] Amer Univ Cairo, Dept Math & Actuarial Sci, Cairo, Egypt

[2] Cornell Univ, Dept Stat Sci, Ithaca, NY USA

[3] Ball State Univ, Dept Math Sci, Muncie, IN USA

来源：

WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS | 2009年 / 1卷 / 01期

关键词：

data mining; density-based outliers; distance-based outliers; Mahalanobis distance; identification of outliers;

D O I：

10.1002/wics.6

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We present an overview of the major developments in the area of detection of outliers. These include projection pursuit approaches as well as Mahalanobis distance-based procedures. We also discuss principal component-based methods, since these are most applicable to the large datasets that have become more prevalent in recent years. The major algorithms within each category are briefly discussed, together with current challenges and possible directions of future research. © 2009 John Wiley & Sons, Inc.

引用

页码：57 / 70

页数：14

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