Machine learningMachine learning

Explainable HDBSCAN

Explainable HDBSCAN combines the hierarchical density-based clustering algorithm HDBSCAN with post-hoc explainability methods — primarily SHAP — to reveal which input features drive cluster membership and separation. It retains HDBSCAN's ability to find clusters of varying shape and density while adding a principled, auditable explanation layer.

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Sources

  1. McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205. DOI: 10.21105/joss.00205
  2. Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. link

Related methods

ScholarGateExplainable HDBSCAN (Explainable Hierarchical Density-Based Spatial Clustering of Applications with Noise). Retrieved 2026-06-04 from https://scholargate.app/en/machine-learning/explainable-hdbscan