قارن الطرق

	توسيع K-means القياسي للتجميع العنقودي عن طريق دمج إشراف جزئي	تجميع K-means
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	2001–2002	1967 (formalized 1982)
صاحب الطريقة	Wagstaff, K. et al. (constrained); Basu, S. et al. (seeded)	MacQueen, J. B.; Lloyd, S. P.
النوع	Semi-supervised clustering	Partitional clustering
المصدر التأسيسي	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.
الأسماء البديلة	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
ذات صلة	5	4
الملخص	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.