Comparer des méthodes
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| HDBSCAN Explicable× | Modèle de mélange gaussien explicable× | |
|---|---|---|
| Domaine | Apprentissage automatique | Apprentissage automatique |
| Famille | Machine learning | Machine learning |
| Année d'origine≠ | 2017–2020 | 1995–2020s |
| Auteur d'origine≠ | McInnes, L.; Healy, J. (HDBSCAN); Lundberg & Lee (SHAP-based explanation) | Reynolds, D. A. & Rose, R. C. (GMM); explainability extensions by various authors |
| Type≠ | Explainable clustering | Probabilistic clustering with post-hoc or built-in explainability |
| Source fondatrice≠ | McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205. DOI ↗ | Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective (Ch. 11 — Mixture Models). MIT Press. ISBN: 978-0-262-01802-9 |
| Alias | XAI-HDBSCAN, Interpretable HDBSCAN, Explainable Hierarchical DBSCAN, HDBSCAN with XAI | X-GMM, Interpretable GMM, Explainable GMM, Transparent Gaussian Mixture Model |
| Apparentées≠ | 6 | 3 |
| Résumé≠ | 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. | An Explainable Gaussian Mixture Model (X-GMM) augments the classical GMM probabilistic clustering framework with transparency mechanisms — such as feature-attribution scores, component-level summaries, or sparse covariance structures — so that discovered clusters and density estimates can be understood, communicated, and audited by human experts. |
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