Compară metode

	K-Nearest Neighbors explicabil	HDBSCAN
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	1967 (KNN); 2010s (explainability extensions)	2013
Autorul original	Cover, T. & Hart, P. (KNN); XAI extensions by various authors	Campello, R. J. G. B.; Moulavi, D.; Sander, J.
Tip	Instance-based learning with explainability layer	Hierarchical density-based clustering
Sursa seminală	Cover, T. & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.	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.
Denumiri alternative	XKNN, Interpretable KNN, Explainable KNN, Transparent K-Nearest Neighbors	HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN*
Înrudite	4	3
Rezumat	Explainable K-Nearest Neighbors (XKNN) augments the classic KNN classifier or regressor with structured post-hoc or built-in explanation mechanisms, exposing which retrieved neighbors, which features, and which distance contributions drive each individual prediction — making the model's reasoning transparent and auditable for human decision-makers.	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.