Módszerek összehasonlítása

	DBSCAN	K-Nearest Neighbors magyarázhatóvá tétele (Explainable K-Nearest Neighbors)
Tudományterület	Gépi tanulás	Gépi tanulás
Módszercsalád	Machine learning	Machine learning
Keletkezés éve	1996	1967 (KNN); 2010s (explainability extensions)
Megalkotó	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Cover, T. & Hart, P. (KNN); XAI extensions by various authors
Típus	Density-based clustering algorithm	Instance-based learning with explainability layer
Alapmű	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.	Cover, T. & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.
Alternatív nevek	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	XKNN, Interpretable KNN, Explainable KNN, Transparent K-Nearest Neighbors
Kapcsolódó	3	4
Összefoglaló	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.	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.