方法对比

	DBSCAN	可解释 K-近邻算法	HDBSCAN
领域	机器学习	机器学习	机器学习
方法族	Machine learning	Machine learning	Machine learning
起源年份	1996	1967 (KNN); 2010s (explainability extensions)	2013
提出者	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Cover, T. & Hart, P. (KNN); XAI extensions by various authors	Campello, R. J. G. B.; Moulavi, D.; Sander, J.
类型	Density-based clustering algorithm	Instance-based learning with explainability layer	Hierarchical density-based clustering
开创性文献	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.	Campello, R. J. G. B., Moulavi, D., & Sander, J. (2013). Density-Based Clustering Based on Hierarchical Density Estimates. In J. Pei et al. (Eds.), Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science, vol. 7819 (pp. 160–172). Springer, Berlin, Heidelberg.
别名	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	XKNN, Interpretable KNN, Explainable KNN, Transparent K-Nearest Neighbors	HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN*
相关	3	4	3
摘要	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.	HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm introduced by Campello, Moulavi, and Sander in 2013. It extends DBSCAN by building a full hierarchy of density-based clusters across all density scales and then extracting a stable flat partition, making it robust to datasets where cluster densities vary substantially across regions.