Abstract
The discussion in the previous chapters focussed on the problem of unsupervised outlier detection in which no prior information is available about the abnormalities in the data. In such scenarios, many of the anomalies found correspond to noise, and may not be of any interest to an analyst. It has been observed [284, 315, 440] in diverse applications such as system anomaly detection, financial fraud, and web robot detection that the nature of the anomalies is often highly specific to particular kinds of abnormal activity in the underlying application.
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© 2013 Springer Science+Business Media New York
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Aggarwal, C.C. (2013). Supervised Outlier Detection. In: Outlier Analysis. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6396-2_6
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DOI: https://doi.org/10.1007/978-1-4614-6396-2_6
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