Skip to main content
SpringerLink
Log in

Outlier Detection

  • Living reference work entry
  • First Online:
Encyclopedia of Database Systems

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,...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Recommended Reading

  1. Hawkins D. Identification of outliers. London: Chapman and Hall; 1980.

    Book  MATH  Google Scholar 

  2. Barnett V, Lewis T. Outliers in statistical data. 3rd ed. Chichester: Wiley; 1994.

    MATH  Google Scholar 

  3. Rousseeuw PJ, Hubert M. Robust statistics for outlier detection. Wiley Interdiscip Rev Data Min Knowl Discov. 2011;1(1):73–9.

    Article  Google Scholar 

  4. Knorr EM, Ng RT, Tucanov V. Distance-based outliers: algorithms and applications. VLDB J. 2000;8(3–4):237–53.

    Article  Google Scholar 

  5. Ramaswamy S, Rastogi R, Shim K. Efficient algorithms for mining outliers from large data sets. In: Proceedings of the ACM International Conference on Management of Data (SIGMOD), Dallas; 2000. p. 427–38.

    Google Scholar 

  6. Angiulli F, Pizzuti C. Outlier mining in large high-dimensional data sets. IEEE Trans Knowl Data Eng. 2005;17(2):203–15.

    Article  MATH  Google Scholar 

  7. Breunig MM, Kriegel HP, Ng RT, Sander J. LOF: identifying density-based local outliers. In: Proceedings of the ACM International Conference on Management of Data (SIGMOD), Dallas; 2000. p. 93–104.

    Google Scholar 

  8. Schubert E, Zimek A, Kriegel HP. Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection. Data Min Knowl Disc. 2014;28(1):190–237.

    Article  MathSciNet  MATH  Google Scholar 

  9. Orair GH, Teixeira C, Wang Y, Meira Jr W, Parthasarathy S. Distance-based outlier detection: consolidation and renewed bearing. Proc VLDB Endowment. 2010;3(2):1469–80.

    Article  Google Scholar 

  10. Zimek A, Schubert E, Kriegel HP. A survey on unsupervised outlier detection in high-dimensional numerical data. Stat Anal Data Min. 2012;5(5): 363–87.

    Article  MathSciNet  Google Scholar 

  11. Zimek A, Campello RJGB, Sander J. Ensembles for unsupervised outlier detection: challenges and research questions. ACM SIGKDD Explor. 2013;15(1):11–22.

    Article  Google Scholar 

  12. Chandola V, Banerjee A, Kumar V. Anomaly detection for discrete sequences: a survey. IEEE Trans Knowl Data Eng. 2012;24(5):823–39.

    Article  Google Scholar 

  13. Akoglu L, Tong H, Koutra D. Graph-based anomaly detection and description: a survey. Data Min Knowl Disc. 2014; doi:10.1007/s10618-014-0365-y.

    Google Scholar 

  14. Kriegel HP, Kröger P, Schubert E, Zimek A. Interpreting and unifying outlier scores. In: Proceedings of the 11th SIAM International Conference on Data Mining (SDM), Mesa; 2011. p. 13–24.

    Google Scholar 

  15. Achtert E, Kriegel HP, Schubert E, Zimek A. Interactive data mining with 3D-parallel-coordinate-trees. In: Proceedings of the ACM International Conference on Management of Data (SIGMOD), New York; 2013. p. 1009–12.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Southern Denmark, Campusvej 55, 5230, Odense, Denmark

    Arthur Zimek

  2. Heidelberg University, Heidelberg, Germany

    Erich Schubert

Authors
  1. Arthur Zimek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Erich Schubert
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arthur Zimek .

Editor information

Editors and Affiliations

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

    Ling Liu

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

    M. Tamer Özsu

Section Editor information

  1. Department of Computer Science and Engineering, The University of California at Riverside, Bourns College of Engineering, 92521, Riverside, CA, USA

    Dimitrios Gunopulos

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media LLC

About this entry

Cite this entry

Zimek, A., Schubert, E. (2017). Outlier Detection. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7993-3_80719-1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4899-7993-3_80719-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4899-7993-3

  • Online ISBN: 978-1-4899-7993-3

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

Publish with us

Policies and ethics

Search

Navigation