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Encyclopedia of Database Systems pp 2635–2639Cite as

Outlier Detection

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Synonyms

Anomaly detection; Fraud detection; Identification of outliers; Rejection of outliers

Definition

Outlier detection aims at identifying those objects in a database that are unusual, i.e., different than the majority of the data and therefore suspicious resulting from a contamination, error, or fraud. In a statistical modeling, the assessment of “being unusual” is typically based on a parametric model of the data, identifying those objects that do not fit well to the modeled distribution as outliers. In the database context, the statistical intuition of “being unusual” is typically modeled in an approximate but more efficient, nonparametric way by (local) density estimates and comparison to some reference set.

Historical Background

Filtering out those observations that look suspiciously different than the majority of observations is a procedure probably tacitly practiced since people studied data collections and tried to make sense out of observations. In the eighteenth century,...

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Recommended Reading

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Author information

Authors and Affiliations

  1. Ludwig-Maximilians-Universität München, Munich, Germany

    Arthur Zimek

  2. Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark

    Arthur Zimek

  3. Heidelberg University, Heidelberg, Germany

    Erich Schubert

Authors
  1. Arthur Zimek
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  2. Erich Schubert
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Corresponding author

Correspondence to Arthur Zimek .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, GA, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, ON, Canada

    M. Tamer Özsu

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Zimek, A., Schubert, E. (2018). Outlier Detection. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_80719

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