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V.J. Hodge and J. Austin (2004)

A survey of outlier detection methodologies

Artificial Intelligence Review, 22:85-126.

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 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
Relevant for: WP5 intrinsic/extrinsic motivations. A review on the techniques for detection of anomalous observations within data (`outlier detetion'), relevant for novelty detection.