Confronta i metodi

	Clustering K-means	Apprendimento semi-supervisionato	Ensemble a votazione
Campo	Apprendimento automatico	Apprendimento automatico	Apprendimento automatico
Famiglia	Machine learning	Machine learning	Machine learning
Anno di origine	1967 (formalized 1982)	1970s–2006 (formalized)	1990s–2004
Ideatore	MacQueen, J. B.; Lloyd, S. P.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Tipo	Partitional clustering	Learning paradigm	Ensemble (combination of multiple classifiers by vote)
Fonte seminale	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Alias	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	SSL, semi-supervised machine learning, transductive learning, label-efficient learning	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Correlati	4	5	5
Sintesi	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 learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.	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.