Methoden vergleichen

	K-means Clustering	Voting Ensemble
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	1967 (formalized 1982)	1990s–2004
Urheber	MacQueen, J. B.; Lloyd, S. P.	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Typ	Partitional clustering	Ensemble (combination of multiple classifiers by vote)
Wegweisende Quelle	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
Aliasnamen	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Verwandt	4	5
Zusammenfassung	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.