Сравнение на методи

	Ансамбъл K-means	Полу-наблюдавано K-средни
Област	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning
Година на възникване	2002	2001–2002
Създател	Strehl, A. & Ghosh, J.	Wagstaff, K. et al. (constrained); Basu, S. et al. (seeded)
Тип	Ensemble clustering (consensus aggregation of K-means partitions)	Semi-supervised clustering
Основополагащ източник	Strehl, A. & Ghosh, J. (2002). Cluster ensembles — a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3, 583–617.	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.
Други названия	consensus K-means, K-means ensemble clustering, cluster ensemble with K-means, EKM	constrained K-means, seeded K-means, partially supervised K-means, SS-K-means
Свързани	3	5
Резюме	Ensemble K-means runs K-means clustering many times under varied initializations, random seeds, or feature subsets, then aggregates the resulting partitions into a single consensus assignment. This approach reduces K-means' well-known sensitivity to initialization and produces more stable, reproducible clusters than any single run.	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.