New Framework Explains Feature-Weighted Clustering with Counterfactuals

This research presents VoICE (Voronoi-Induced Counterfactual Explainability), a novel framework designed to provide local, interpretable explanations for feature-weighted k-means clustering. Unlike traditional counterfactual methods primarily used in supervised learning, VoICE addresses the unique challenges of clustering by considering the geometric properties of the partition. It formulates counterfactual generation as a projection onto the full weighted Voronoi region of a target cluster, directly integrating feature weights into both the clustering geometry and the counterfactual objective. The framework aims to produce least-cost and parsimonious explanations, subject to actionability constraints. To prevent extrapolation and boundary sensitivity, target regions are further refined by intersecting them with data-derived bounds and contracting them towards their centroids. Evaluations across several benchmark datasets demonstrate that VoICE consistently generates valid target-cluster memberships, a significant improvement over leading pairwise baselines that often fail in this regard. This advancement offers a more robust and accurate method for understanding why a data point belongs to a particular cluster and what minimal changes would shift its assignment.