Beyond Numerical – MIXATON for outlier explanation on mixed-type data (ADPBD)

Abstract

Outlier explanation approaches are employed to support analysts in investigating outliers, especially those detected by methods which are not intuitively interpretable such as deep learning or ensemble approaches. There have been several studies on outlier explanation in the last years. Nonetheless, there have been no outlier explanation approaches for mixed-type data. In this paper we propose multiple approaches for outlier explanation on mixed-type data. We benchmark them by using synthetic outlier datasets and by generating ground-truth explanation for real-world outlier datasets. The results on the various datasets show that while there is no approach that dominates others for all types of outliers and datasets, some can offer a consistently high performance.