1 research outputs found
Coverage-based Outlier Explanation
Outlier detection is a core task in data mining with a plethora of algorithms
that have enjoyed wide scale usage. Existing algorithms are primarily focused
on detection, that is the identification of outliers in a given dataset. In
this paper we explore the relatively under-studied problem of the outlier
explanation problem. Our goal is, given a dataset that is already divided into
outliers and normal instances, explain what characterizes the outliers. We
explore the novel direction of a semantic explanation that a domain expert or
policy maker is able to understand. We formulate this as an optimization
problem to find explanations that are both interpretable and pure. Through
experiments on real-world data sets, we quantitatively show that our method can
efficiently generate better explanations compared with rule-based learners