Comparar métodos

	Vecinos más Cercanos Explicables (Explainable K-Nearest Neighbors)	HDBSCAN
Campo	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Machine learning
Año de origen	1967 (KNN); 2010s (explainability extensions)	2013
Autor original	Cover, T. & Hart, P. (KNN); XAI extensions by various authors	Campello, R. J. G. B.; Moulavi, D.; Sander, J.
Tipo	Instance-based learning with explainability layer	Hierarchical density-based clustering
Fuente seminal	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.
Alias	XKNN, Interpretable KNN, Explainable KNN, Transparent K-Nearest Neighbors	HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN*
Relacionados	4	3
Resumen	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.