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Outlier detection
Author(s) -
Su Xiaogang,
Tsai ChihLing
Publication year - 2011
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.19
Subject(s) - outlier , anomaly detection , computer science , cluster analysis , data science , key (lock) , scalability , data mining , artificial intelligence , database , computer security
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