เปรียบเทียบวิธี

	Explainable DBSCAN	DBSCAN
สาขาวิชา	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	1996 (DBSCAN); 2010s (XAI integration)	1996
ผู้ริเริ่ม	Ester, M. et al. (DBSCAN); XAI layer via Lundberg & Lee (SHAP)	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.
ประเภท	Unsupervised clustering with post-hoc interpretability	Density-based clustering algorithm
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	XAI-DBSCAN, interpretable DBSCAN, transparent density clustering, DBSCAN with post-hoc explanation	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering
ที่เกี่ยวข้อง	5	3
สรุป	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.