Bandingkan metode

	DBSCAN yang Dapat Dijelaskan	K-Tetangga Terdekat yang Dapat Dijelaskan
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	1996 (DBSCAN); 2010s (XAI integration)	1967 (KNN); 2010s (explainability extensions)
Pencetus	Ester, M. et al. (DBSCAN); XAI layer via Lundberg & Lee (SHAP)	Cover, T. & Hart, P. (KNN); XAI extensions by various authors
Tipe	Unsupervised clustering with post-hoc interpretability	Instance-based learning with explainability layer
Sumber perintis	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.
Alias	XAI-DBSCAN, interpretable DBSCAN, transparent density clustering, DBSCAN with post-hoc explanation	XKNN, Interpretable KNN, Explainable KNN, Transparent K-Nearest Neighbors
Terkait	5	4
Ringkasan	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.