방법 비교

	준지도 학습 K-평균 (Semi-supervised K-means)	DBSCAN
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2001–2002	1996
창시자	Wagstaff, K. et al. (constrained); Basu, S. et al. (seeded)	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.
유형	Semi-supervised clustering	Density-based clustering algorithm
원전	Wagstaff, K., Cardie, C., Rogers, S., & Schroedl, S. (2001). Constrained K-means Clustering with Background Knowledge. In Proceedings of the 18th International Conference on Machine Learning (ICML 2001), pp. 577–584.	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.
별칭	constrained K-means, seeded K-means, partially supervised K-means, SS-K-means	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering
관련	5	3
요약	Semi-supervised K-means extends standard K-means clustering by incorporating partial supervision — either a small set of labeled seed points or pairwise must-link and cannot-link constraints — to guide cluster formation. It bridges unsupervised clustering and fully supervised classification, enabling more meaningful clusters when labels are scarce but costly to obtain in full.	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.