Compară metode

	Clustering K-means	DBSCAN semi-supervizat
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	1967 (formalized 1982)	2000s
Autorul original	MacQueen, J. B.; Lloyd, S. P.	Ester, M. et al. (DBSCAN base); semi-supervised extensions by multiple authors (2000s–2010s)
Tip	Partitional clustering	Constrained density-based clustering
Sursa seminală	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.	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.
Denumiri alternative	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	Constrained DBSCAN, SS-DBSCAN, DBSCAN with must-link/cannot-link constraints, seeded DBSCAN
Înrudite	4	5
Rezumat	K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis.	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.