用反事实推理解释DBSCAN聚类结果，让聚类决策更透明可信。
ExDBSCAN: Explaining DBSCAN with Counterfactual Reasoning -- Additional Material
- 基于密度连通图生成反事实样本，模拟点在不同条件下的归属变化。
- 在30个数据集上验证，所有解释均有效且能生成多样、贴近原点的反事实。
- 适合需要理解聚类结果可靠性或调试异常点的科研与工程人员。
聚类是根据相似性对数据点进行分组的无监督技术。尽管监督学习已有可解释性方法，但难以直接应用于聚类，导致难以理解聚类分配的原因。这一可解释性差距在流行的基于密度的DBSCAN方法中尤为明显，该方法将点划分为内点（密集区域成员）或离群点（稀疏区域噪声）。DBSCAN无法说明某一点为何被赋予特定分配，也无法判断该分配是否对数据微小变化具有鲁棒性。为弥补这一不足，我们提出ExDBSCAN，一种密度感知的后处理解释方法。ExDBSCAN提供可操作的反事实解释，并具备理论有效性保证。它通过密度连通加权图生成多个反事实，采用受物理启发的模型：反事实样本间相互排斥（保持多样性），同时被拉向待解释实例（保持接近性）。在30个表格数据集上与四种基线对比的实证评估显示，ExDBSCAN优于所有基线，在保证完全有效性的同时，成功检索到多样且贴近原点的反事实。
原文摘要 · Abstract (English)
Clustering is an unsupervised technique for grouping data points by similarity. While explainability methods exist for supervised machine learning, they are not directly applicable to clustering, making it challenging to understand cluster assignments. This interpretability gap is particularly evident in the popular density-based method DBSCAN, which assigns points as inliers (cluster members in dense regions) or outliers (noise points in sparse regions). DBSCAN does not provide insight into why a particular point receives its assignment or whether its assignment is robust to small changes in the data. To address the lack of explainability, we introduce ExDBSCAN, a density-aware, post-hoc explanation method. ExDBSCAN offers actionable counterfactual explanations, with theoretical guarantees for validity. It generates multiple counterfactuals using a density connected weighted graph, adopting a physics-inspired model that repels counterfactual candidates from one another (diversity), while pulling them toward the instance to explain (proximity). Empirical evaluation on 30 tabular datasets comparing against four baselines shows that ExDBSCAN outperforms all baselines while attaining perfect validity and retrieving diverse, proximal counterfactuals.
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