Methoden vergleichen

	Semi-überwachtes K-Means	K-means Clustering
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2001–2002	1967 (formalized 1982)
Urheber	Wagstaff, K. et al. (constrained); Basu, S. et al. (seeded)	MacQueen, J. B.; Lloyd, S. P.
Typ	Semi-supervised clustering	Partitional clustering
Wegweisende Quelle	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.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
Aliasnamen	constrained K-means, seeded K-means, partially supervised K-means, SS-K-means	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Verwandt	5	4
Zusammenfassung	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.	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.