A survey of outlier detection methodologies

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A survey of outlier detection methodologies (2004)

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by Victoria J. Hodge , Jim Austin

Venue:	Artificial Intelligence Review
Citations:	312 - 3 self

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@ARTICLE{Hodge04asurvey,
    author = {Victoria J. Hodge and Jim Austin},
    title = {A survey of outlier detection methodologies},
    journal = {Artificial Intelligence Review},
    year = {2004},
    volume = {22},
    pages = {2004}
}

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Abstract

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.

Keyphrases

detection methodology systematic technique mechanical fault instrument error outlier detection system fault detection method system behaviour catastrophic consequence data set anomalous observation comparative review fraudulent behaviour natural deviation contemporary technique human error full gamut respective motivation computer science

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