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A Survey of Outlier Detection Methodologies

Artificial Intelligence Review volume 22, pages 85–126 (2004)Cite this article

Abstract

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.

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Authors and Affiliations

  1. Department of Computer Science, University of York, York, YO10 5DD, UK

    Victoria J. Hodge & Jim Austin

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  1. Victoria J. Hodge
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  2. Jim Austin
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Correspondence to Jim Austin.

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Hodge, V.J., Austin, J. A Survey of Outlier Detection Methodologies. Artif Intell Rev 22, 85–126 (2004). https://doi.org/10.1007/s10462-004-4304-y

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  • DOI: https://doi.org/10.1007/s10462-004-4304-y

Keywords

  • anomaly
  • detection
  • deviation
  • noise
  • novelty
  • outlier
  • recognition