Interpreting and Unifying Outlier Scores

H.-P. Kriegel, P. Kröger, E. Schubert, A. Zimek
In Proceedings of the 11th SIAM International Conference on Data Mining (SDM), Mesa, AZ: 13–24, 2011.


Outlier scores provided by different outlier models differ widely in their meaning, range, and contrast between different outlier models and, hence, are not easily comparable or interpretable. We propose a unification of outlier scores provided by various outlier models and a translation of the arbitrary "outlier factors" to values in the range [0, 1] interpretable as values describing the probability of a data object of being an outlier. As an application, we show that this unification facilitates enhanced ensembles for outlier detection.


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