قارن الطرق

	DBSCAN	HDBSCAN
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	1996	2013
صاحب الطريقة	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Campello, R. J. G. B.; Moulavi, D.; Sander, J.
النوع	Density-based clustering algorithm	Hierarchical density-based clustering
المصدر التأسيسي	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231.	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	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN*
ذات صلة	3	3
الملخص	DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes.	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.