เปรียบเทียบวิธี

	DBSCAN แบบกึ่งมีผู้สอน	DBSCAN
สาขาวิชา	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	2000s	1996
ผู้ริเริ่ม	Ester, M. et al. (DBSCAN base); semi-supervised extensions by multiple authors (2000s–2010s)	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.
ประเภท	Constrained density-based clustering	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 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), pp. 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	Constrained DBSCAN, SS-DBSCAN, DBSCAN with must-link/cannot-link constraints, seeded DBSCAN	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering
ที่เกี่ยวข้อง	5	3
สรุป	Semi-supervised DBSCAN extends the canonical density-based clustering algorithm (Ester et al., 1996) by incorporating a small set of pairwise or label constraints — must-link pairs that must share a cluster, cannot-link pairs that must be separated, or a handful of known labels — to guide cluster formation while retaining DBSCAN's ability to discover arbitrary-shaped clusters and flag noise points.	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.