为加权k均值聚类设计可解释的反事实生成方法，提升解释有效性。
Counterfactuals for Feature-Weighted Clustering
- 基于加权维诺区域投影生成反事实，融合特征权重与可行动约束。
- 在多个基准数据集上成功生成有效目标簇归属，优于传统成对边界方法。
- 适合需要可解释聚类结果的应用场景，如医疗、金融等决策支持。
反事实解释通过识别改变输入即可改变其归属结果的条件，提供局部可解释性。尽管在监督学习中已广泛应用，但将其拓展至聚类仍具挑战，因聚类标签未标注且由划分几何结构决定。本文提出VoICE框架——一种针对特征加权k均值聚类的维诺诱导反事实可解释性方法。不同于仅跨越单个成对质心边界的处理方式，VoICE将反事实生成建模为投影至目标簇的完整加权维诺区域，直接将特征权重融入聚类几何与反事实目标，从而在可行动性约束下获得最小代价、简洁的解释。目标区域进一步与数据导出的边界相交，并向质心同向缩放，降低外推风险与边界敏感性。实验表明，VoICE在多个基准数据集上始终生成有效的目标簇成员，而主流成对基线方法未能实现。
原文摘要 · Abstract (English)
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.
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