Outliers are extraordinary objects in a data collection. Depending on the domain, they may represent errors,
fraudulent activities or rare events that are subject of our interest.
Existing approaches focus on detection of outliers or degrees of outlierness (ranking), but do not provide a possible explanation
of how these objects deviate from the rest of the data. Such explanations would help user to interpret or validate the detected
outliers.

The problem addressed in this paper is as follows: given an outlier detected by an existing algorithm, we propose a method that determines possible explanations for the outlier. These explanations are expressed in the form of subspaces in which the given outlier shows separability from the inliers. In this manner, our proposed method complements existing outlier detection algorithms by providing additional information about the outliers. Our method is designed to work with any existing
outlier detection algorithm and it also includes a heuristic that
gives a substantial speedup over the baseline strategy.