Explaining Data Regularities and Anomalies

In the spirit of explainable AI approaches, this paper introduces a new strategy whose aim is to linguistically describe the inner structure of a dataset. Instead of removing irregular points and focusing on the analysis of regular points, the proposed approach relies on a unified data structure, an isolation forest, to both separate regular from irregular points and to identify their inner structure using a data-driven similarity measure. In addition, clusters of regular and irregular points are then linguistically described so as to help users focus on the most characteristic properties of each cluster and to possibly understand the reason why some points are irregular.