Supervised Outlier Detection

  • Charu C. Aggarwal


The discussions in the previous chapters focus 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 or other uninteresting phenomena. It has been observed [338, 374, 531] in diverse applications such as system anomaly detection, financial fraud, and Web robot detection that interesting anomalies are often highly specific to particular types of abnormal activity in the underlying application. In such cases, an unsupervised outlier detection method might discover noise, which is not specific to that activity, and therefore may not be of interest to an analyst. In many cases, different types of abnormal instances could be present, and it may be desirable to distinguish among them. For example, in an intrusion-detection scenario, different types of intrusion anomalies are possible, and the specific type of an intrusion is important information.


Random Forest Outlier Detection Test Instance Decision Boundary Unlabeled Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Charu C. Aggarwal
    • 1
  1. 1.IBM T.J. Watson Research CenterNew YorkUSA

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