เปรียบเทียบวิธี

	Online K-means	DBSCAN
สาขาวิชา	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	1967 (online update rule); 2010 (mini-batch variant)	1996
ผู้ริเริ่ม	MacQueen, J. (batch); Sculley, D. (mini-batch web-scale variant)	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.
ประเภท	Unsupervised clustering (online/streaming)	Density-based clustering algorithm
แหล่งต้นตำรับ	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.	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	sequential k-means, streaming k-means, incremental k-means, online clustering	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering
ที่เกี่ยวข้อง	4	3
สรุป	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.	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.