Salīdzināt metodes

	K-means klasterizācija	Balsošanas ansamblis
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1967 (formalized 1982)	1990s–2004
Autors	MacQueen, J. B.; Lloyd, S. P.	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Tips	Partitional clustering	Ensemble (combination of multiple classifiers by vote)
Pirmavots	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
Citi nosaukumi	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Saistītās	4	5
Kopsavilkums	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.