Compară metode

	Clustering K-means	Ansamblul de votare
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	1967 (formalized 1982)	1990s–2004
Autorul original	MacQueen, J. B.; Lloyd, S. P.	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Tip	Partitional clustering	Ensemble (combination of multiple classifiers by vote)
Sursa seminală	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Denumiri alternative	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
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.	A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted.