방법 비교

	설명 가능한 DBSCAN	설명 가능한 K-최근접 이웃 (Explainable K-Nearest Neighbors, XKNN)
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	1996 (DBSCAN); 2010s (XAI integration)	1967 (KNN); 2010s (explainability extensions)
창시자	Ester, M. et al. (DBSCAN); XAI layer via Lundberg & Lee (SHAP)	Cover, T. & Hart, P. (KNN); XAI extensions by various authors
유형	Unsupervised clustering with post-hoc interpretability	Instance-based learning with explainability layer
원전	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.
별칭	XAI-DBSCAN, interpretable DBSCAN, transparent density clustering, DBSCAN with post-hoc explanation	XKNN, Interpretable KNN, Explainable KNN, Transparent K-Nearest Neighbors
관련	5	4
요약	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.