Mining multidimensional contextual outliers from categorical relational data

Abstract

A wide range of methods have been proposed for detecting different types of outliers in full space and subspaces. However, the interpretability of outliers, that is, explaining in what ways and to what extent an object is an outlier, remains a critical open issue. In this paper, we develop a notion of contextual outliers on categorical data. Intuitively, a contextual outlier is a small group of objects that share strong similarity with a significantly larger reference group of objects on some attributes, but deviate dramatically on some other attributes. We develop a detection algorithm, and conduct experiments to evaluate our approach.