Explainable clustering with multidimensional bounding boxes

Abstract

Explainable Artificial Intelligence (XAI) aims at introducing transparency and intelligibility into decision-making process of AI systems. Most of the work in this area is focused on supervised machine learning tasks such as classification and regression. Unsupervised algorithms such as clustering can also be explained with existing approaches. This is most often achieved by explaining a classifier trained on cluster data with cluster labels as a dependant variable. However, with such a transformation the information about cluster shape and distribution is lost, which may lead to wrong interpretation of explanations. In this paper, we introduce a method that aids end experts in cluster analysis with human-readable rule-based explanations. We use state-of-the-art explanation mechanism on the multidimensional bounding boxes that represent arbitrarily-shaped clusters. We demonstrate our approach on reproducible synthetic datasets.