A survey of outlier detection methodologies

被引：1968

作者：

Hodge V.J. ^{[1
]}

Austin J. ^{[1
]}

机构：

[1] Department of Computer Science, University of York, York

来源：

Artificial Intelligence Review | 2004年 / 22卷 / 2期

关键词：

Anomaly; Detection; Deviation; Noise; Novelty; Outlier; Recognition;

D O I：

10.1023/B:AIRE.0000045502.10941.a9

中图分类号：

学科分类号：

摘要：

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.

引用

页码：85 / 126

页数：41

共 67 条

[1]

Aggarwal C.C., Yu P.S., Outlier detection for high dimensional data, Proceedings of the ACM SIGMOD Conference 2001, (2001)

[2]

Aha D.W., Bankert R.B., Feature selection for case-based classification of cloud types: An empirical comparison, Proceedings of the AAAI-94 Workshop on Case-based Reasoning, (1994)

[3]

Allan J., Carbonell J., Doddington G., Yamron J., Yang Y., Topic detection and tracking pilot study: Final report, Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop, (1998)

[4]

Arning A., Agrawal R., Raghavan P., A linear method for deviation detection in large databases, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 164-169, (1996)

[5]

Baker L.D., Hofmann T., McCallum A.K., Yang Y., A hierarchical probabilistic model for novelty detection in text, NIPS'99, (1999)

[6]

Barnett V., Lewis T., Outliers in Statistical Data, 3rd Edn., (1994)

[7]

Beale R., Jackson T., Neural Computing: An Introduction, (1990)

[8]

Bishop C.M., Novelty detection & neural network validation, Proceedings of the IEE Conference on Vision, Image and Signal Processing, pp. 217-222, (1994)

[9]

Bishop C.M., Neural Networks for Pattern Recognition, (1995)

[10]

Blake C.L., Merz C.J., UCI Repository of Machine Learning Databases, (1998)

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