ArticleEntropy (Basel, Switzerland)2026
Causal Interpretation of DBSCAN Algorithm: A Dynamic Modeling for Epsilon Estimation.
Article in Entropy (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
DBSCAN is widely used to identify structured regions in unlabeled data, but its performance depends critically on the selection of the neighborhood parameter ε. Traditional heuristics for estimating ε often become unreliable in high-dimensional or varying-density settings because they rely heavily on local geometric criteria and may fail under smooth transitions or topological ambiguity. This work presents a three-level perspective on DBSCAN hyperparameter selection. At the algorithmic level, ε controls neighborhood connectivity and structural transitions in clustering. At the modeling level, the ordered
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