방법 비교

	DBSCAN	HDBSCAN	온라인 K-평균
분야	머신러닝	머신러닝	머신러닝
계열	Machine learning	Machine learning	Machine learning
기원 연도	1996	2013	1967 (online update rule); 2010 (mini-batch variant)
창시자	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Campello, R. J. G. B.; Moulavi, D.; Sander, J.	MacQueen, J. (batch); Sculley, D. (mini-batch web-scale variant)
유형	Density-based clustering algorithm	Hierarchical density-based clustering	Unsupervised clustering (online/streaming)
원전	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.	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.	MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, pp. 281–297. University of California Press.
별칭	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN*	sequential k-means, streaming k-means, incremental k-means, online clustering
관련	3	3	4
요약	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.	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.	Online K-means is a streaming variant of the classical K-means algorithm that updates cluster centroids one observation at a time — or in small mini-batches — without storing the entire dataset in memory. It is particularly suited to large-scale, real-time, or continuously arriving data where batch recomputation would be too slow or impractical.