قارن الطرق

	HDBSCAN	التجميع الهرمي الكثافي شبه المُشرف عليه (Semi-supervised HDBSCAN)
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	2013	2017–present
صاحب الطريقة	Campello, R. J. G. B.; Moulavi, D.; Sander, J.	McInnes, L.; Healy, J. (base HDBSCAN); semi-supervised extensions by various authors
النوع	Hierarchical density-based clustering	Semi-supervised density-based clustering
المصدر التأسيسي	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.	McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.
الأسماء البديلة	HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN*	Constrained HDBSCAN, Semi-supervised hierarchical density clustering, HDBSCAN with partial labels, SS-HDBSCAN
ذات صلة	3	6
الملخص	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.	Semi-supervised HDBSCAN extends the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm by incorporating partial supervision — such as must-link and cannot-link pairwise constraints or a small set of labeled examples — to guide the density-based cluster hierarchy toward cluster assignments that are consistent with available domain knowledge.