AbstractOutlier 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 Discov2011 1 261–268 DOI: 10.1002/widm.19This article is categorized under:Algorithmic Development > Scalable Statistical MethodsFundamental Concepts of Data and Knowledge > Motivation and Emergence of Data MiningAlgorithmic Development > StatisticsTechnologies > Structure Discovery and Clustering