Outlier Detection in Arbitrarily Oriented Subspaces

In this paper, we propose a novel outlier detection model to find outliers that deviate from the generating mechanisms of normal instances by considering combinations of different subsets of attributes, as they occur when there are local correlations in the data set. Our model enables to search for outliers in arbitrarily oriented subspaces of the original feature space. We show how in addition to an outlier score, our model also derives an explanation of the outlierness that is useful in investigating the results. Our experiments suggest that our novel method can find different outliers than existing work and can be seen as a complement of those approaches.