השוואת שיטות

	DBSCAN	K-Nearest Neighbors מוסבר (Explainable K-Nearest Neighbors)
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	1996	1967 (KNN); 2010s (explainability extensions)
הוגה השיטה	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Cover, T. & Hart, P. (KNN); XAI extensions by various authors
סוג	Density-based clustering algorithm	Instance-based learning with explainability layer
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. 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.
כינויים	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	XKNN, Interpretable KNN, Explainable KNN, Transparent K-Nearest Neighbors
קשורות	3	4
תקציר	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.