AI RESEARCH
ExDBSCAN: Explaining DBSCAN with Counterfactual Reasoning -- Additional Material
arXiv CS.LG
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ArXi:2605.30225v1 Announce Type: new 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.