A Survey of Outlier Detection Methodologies

@article{Hodge2004ASO,
  title={A Survey of Outlier Detection Methodologies},
  author={Victoria J. Hodge and Jim Austin},
  journal={Artificial Intelligence Review},
  year={2004},
  volume={22},
  pages={85-126}
}
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… 

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