Outlier detection

Outlier detection

Outlier detection is an area of research with a long history which has applications in many fields. This article provides a nontechnical and concise overview of the commonly used approaches for detecting outliers, including classical methods, new challenges posed by real‐world massive data, and some...

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Veröffentlicht in:Wiley interdisciplinary reviews. Data mining and knowledge discovery 2011-05, Vol.1 (3), p.261-268
Hauptverfasser: Su, Xiaogang, Tsai, Chih-Ling
Format: Artikel
Sprache:eng
Schlagworte:
Clustering
Data analysis
Data mining
Outliers (statistics)
Statistical methods
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Zusammenfassung:Outlier detection is an area of research with a long history which has applications in many fields. This article provides a nontechnical and concise overview of the commonly used approaches for detecting outliers, including classical methods, new challenges posed by real‐world massive data, and some of the key advances made in recent years. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 261–268 DOI: 10.1002/widm.19 This article is categorized under: Algorithmic Development > Scalable Statistical Methods Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining Algorithmic Development > Statistics Technologies > Structure Discovery and Clustering
ISSN:1942-4787
1942-4795
DOI:10.1002/widm.19