So sánh phương pháp

	DBSCAN có thể giải thích	K-Lân Cận Gần Nhất Có Thể Giải Thích
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	1996 (DBSCAN); 2010s (XAI integration)	1967 (KNN); 2010s (explainability extensions)
Người khởi xướng	Ester, M. et al. (DBSCAN); XAI layer via Lundberg & Lee (SHAP)	Cover, T. & Hart, P. (KNN); XAI extensions by various authors
Loại	Unsupervised clustering with post-hoc interpretability	Instance-based learning with explainability layer
Công trình gốc	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.	Cover, T. & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.
Tên gọi khác	XAI-DBSCAN, interpretable DBSCAN, transparent density clustering, DBSCAN with post-hoc explanation	XKNN, Interpretable KNN, Explainable KNN, Transparent K-Nearest Neighbors
Liên quan	5	4
Tóm tắt	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.	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.