Outlier detection
Authors:
- 9 March 2011
- journal article
- research article
- Published by Wiley in WIREs Data Mining and Knowledge Discovery
- Vol. 1 (3) , 261-268
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 Discov2011 1 261–268 DOI: 10.1002/widm.19This 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 ClusteringKeywords
This publication has 51 references indexed in Scilit:
- Data warehousing and data mining techniques for intrusion detection systemsDistributed and Parallel Databases, 2006
- Fast Distributed Outlier Detection in Mixed-Attribute Data SetsData Mining and Knowledge Discovery, 2006
- Outliers in multivariate time seriesBiometrika, 2000
- Distance-based outliers: algorithms and applicationsThe VLDB Journal, 2000
- Identification of Outliers in Multivariate DataJournal of the American Statistical Association, 1996
- Procedures for the Identification of Multiple Outliers in Linear ModelsJournal of the American Statistical Association, 1993
- The Identification of Multiple OutliersJournal of the American Statistical Association, 1993
- Breakdown Points of Affine Equivariant Estimators of Multivariate Location and Covariance MatricesThe Annals of Statistics, 1991
- Masking Effect on Tests for Outliers in Exponential ModelsJournal of the American Statistical Association, 1985
- Sample Criteria for Testing Outlying ObservationsThe Annals of Mathematical Statistics, 1950