विधियों की तुलना करें

	के-मीन्स क्लस्टरिंग	अर्ध-पर्यवेक्षित शिक्षण	मतदान एनसेंबल
क्षेत्र	मशीन अधिगम	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning	Machine learning
उद्भव वर्ष	1967 (formalized 1982)	1970s–2006 (formalized)	1990s–2004
प्रवर्तक	MacQueen, J. B.; Lloyd, S. P.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)	Lam & Suen; Kuncheva, L. I. (systematic treatment)
प्रकार	Partitional clustering	Learning paradigm	Ensemble (combination of multiple classifiers by vote)
मौलिक स्रोत	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
उपनाम	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
संबंधित	4	5	5
सारांश	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.