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WIREs Data Mining Knowl Discov
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Outlier detection

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Abstract 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

Box plot with outliers.

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Outliers in data of varying density.

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Scatter plot with an outlier (point O) that cannot be detected via univariate techniques.

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Technologies > Structure Discovery and Clustering
Algorithmic Development > Statistics
Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining
Algorithmic Development > Scalable Statistical Methods

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