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Detection of outliers
Author(s) -
Hadi Ali S.,
Imon A. H. M. Rahmatullah,
Werner Mark
Publication year - 2009
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.6
Subject(s) - mahalanobis distance , outlier , principal component analysis , computer science , projection pursuit , robust principal component analysis , data mining , artificial intelligence , anomaly detection , machine learning , pattern recognition (psychology)
Abstract 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. Copyright © 2009 John Wiley & Sons, Inc. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Robust Methods