قارن الطرق

	Explainable HDBSCAN	HDBSCAN
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	2017–2020	2013
صاحب الطريقة	McInnes, L.; Healy, J. (HDBSCAN); Lundberg & Lee (SHAP-based explanation)	Campello, R. J. G. B.; Moulavi, D.; Sander, J.
النوع	Explainable clustering	Hierarchical density-based clustering
المصدر التأسيسي	McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.	Campello, R. J. G. B., Moulavi, D., & Sander, J. (2013). Density-Based Clustering Based on Hierarchical Density Estimates. In J. Pei et al. (Eds.), Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science, vol. 7819 (pp. 160–172). Springer, Berlin, Heidelberg.
الأسماء البديلة	XAI-HDBSCAN, Interpretable HDBSCAN, Explainable Hierarchical DBSCAN, HDBSCAN with XAI	HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN*
ذات صلة	6	3
الملخص	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.	HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm introduced by Campello, Moulavi, and Sander in 2013. It extends DBSCAN by building a full hierarchy of density-based clusters across all density scales and then extracting a stable flat partition, making it robust to datasets where cluster densities vary substantially across regions.