Contribution to a conference proceedings/Contribution to a book

FZJ-2026-04958

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png ExDBSCAN: Explaining DBSCAN with Counterfactual Reasoning

Matthews, P. ; Krieger, L.FZJ* ; Amico, T. ; Zimek, A. ; Seidl, T. ; Assent, I.FZJ*

2026
ACM New York, NY, USA

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Please use a persistent id in citations: doi:10.1145/3770855.3817692  doi:10.34734/FZJ-2026-04958

Abstract: 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 if it 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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Contributing Institute(s):

1. Datenanalyse und Maschinenlernen (IAS-8)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)

Appears in the scientific report 2026

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