Artificial Intelligence Review

, Volume 22, Issue 2, pp 85–126

A Survey of Outlier Detection Methodologies

  • Victoria Hodge
  • Jim Austin

DOI: 10.1023/B:AIRE.0000045502.10941.a9

Cite this article as:
Hodge, V. & Austin, J. Artificial Intelligence Review (2004) 22: 85. doi:10.1023/B:AIRE.0000045502.10941.a9


Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review.

anomaly detection deviation noise novelty outlier recognition 

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Victoria Hodge
    • 1
  • Jim Austin
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkUK

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