השוואת שיטות

	DBSCAN מוסבר	HDBSCAN
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	1996 (DBSCAN); 2010s (XAI integration)	2013
הוגה השיטה	Ester, M. et al. (DBSCAN); XAI layer via Lundberg & Lee (SHAP)	Campello, R. J. G. B.; Moulavi, D.; Sander, J.
סוג	Unsupervised clustering with post-hoc interpretability	Hierarchical density-based clustering
מקור מכונן	Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), 226–231. AAAI Press.	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.
כינויים	XAI-DBSCAN, interpretable DBSCAN, transparent density clustering, DBSCAN with post-hoc explanation	HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN*
קשורות	5	3
תקציר	Explainable DBSCAN pairs the DBSCAN density-based clustering algorithm with post-hoc interpretability methods — most commonly SHAP values or local surrogate models — to reveal which input features drive the algorithm's cluster and noise assignments. It enables analysts to understand why specific points were grouped together or flagged as outliers, bridging the gap between powerful density-based partitioning and human-readable explanation.	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.