发表机构
School of Computer Science and Electronic Engineering, University of Essex（埃塞克斯大学计算机科学与电子工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究特征加权聚类的反事实解释问题，提出VoICE框架，通过将反事实生成投影到目标聚类的加权Voronoi区域，结合特征权重和数据衍生边界，在多个基准数据集上能有效产生目标聚类成员关系。
AI 中文摘要
反事实解释通过识别输入的变化来提供局部、可解释的见解，这在监督学习中已得到充分确立，但扩展到聚类则不太直接，因为聚类分配是无标签的且受分区几何结构支配。本文介绍了VoICE，这是一种用于特征加权k均值聚类的Voronoi诱导反事实可解释性框架。VoICE将反事实生成制定为投影到目标聚类的全加权Voronoi区域上，将特征权重直接纳入聚类几何结构和反事实目标中，以在可操作性约束下产生成本最低且简洁的解释。目标区域进一步与数据衍生边界相交并向其质心进行相似收缩，限制外推和边界敏感性。在几个基准数据集上，VoICE始终能产生有效的目标聚类成员关系，而领先的成对基线则不能。
英文摘要
Counterfactual explanations provide local, interpretable insight by identifying changes to an input that would alter its assigned outcome. Although well established in supervised learning, their extension to clustering is less direct, since cluster assignments are unlabeled and governed by the geometry of the partition. This paper introduces VoICE, a Voronoi-Induced Counterfactual Explainability framework for feature-weighted $k$-means clustering. Rather than treating cluster change as a crossing of a single pairwise centroid boundary, VoICE formulates counterfactual generation as projection onto the full weighted Voronoi region of a target cluster, incorporating feature weights directly into both the clustering geometry and the counterfactual objective to yield least-cost and parsimonious explanations under actionability constraints. Target regions are further intersected with data-derived bounds and homothetically contracted towards their centroids, limiting extrapolation and boundary sensitivity. VoICE consistently produces valid target-cluster membership, across several benchmark datasets, where the leading pairwise baseline does not.
