Comparer des méthodes

	DBSCAN explicable	DBSCAN	K-Plus-Proches-Voisins Explicable
Domaine	Apprentissage automatique	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning	Machine learning
Année d'origine	1996 (DBSCAN); 2010s (XAI integration)	1996	1967 (KNN); 2010s (explainability extensions)
Auteur d'origine	Ester, M. et al. (DBSCAN); XAI layer via Lundberg & Lee (SHAP)	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Cover, T. & Hart, P. (KNN); XAI extensions by various authors
Type	Unsupervised clustering with post-hoc interpretability	Density-based clustering algorithm	Instance-based learning with explainability layer
Source fondatrice	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.	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.
Alias	XAI-DBSCAN, interpretable DBSCAN, transparent density clustering, DBSCAN with post-hoc explanation	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	XKNN, Interpretable KNN, Explainable KNN, Transparent K-Nearest Neighbors
Apparentées	5	3	4
Résumé	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.	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.