Semi-supervised outlier detection

Authors:
Jing Gao;Haibin Cheng;Pang-Ning Tan
Affiliations:
Michigan State University, East Lansing, MI;Michigan State University, East Lansing, MI;Michigan State University, East Lansing, MI
Venue:
Proceedings of the 2006 ACM symposium on Applied computing
Year:
2006

Citing 4
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Abstract

Outlier detection has been extensively researched in the context of unsupervised learning. But the learning results are not always satisfactory, which can be significantly improved using supervision of some labeled points. In this paper, we are concerned with employing supervision of limited amount of label information to detect outliers more accurately. The key of our approach is an objective function that punishes poor clustering results and deviation from known labels as well as restricts the number of outliers. The outliers can be found as a solution to the discrete optimization problem regarding the objective function. By this way, this method can detect meaningful outliers that can not be identified by existing unsupervised methods.