Confronta i metodi

	UMAP	Clustering K-means	Random Forest
Campo	Apprendimento automatico	Apprendimento automatico	Apprendimento automatico
Famiglia	Machine learning	Machine learning	Machine learning
Anno di origine	2018	1967 (formalized 1982)	2001
Ideatore	McInnes, L.; Healy, J.; Melville, J.	MacQueen, J. B.; Lloyd, S. P.	Breiman, L.
Tipo	Nonlinear manifold-learning dimension reduction	Partitional clustering	Ensemble (bagging of decision trees)
Fonte seminale	McInnes, L., Healy, J. & Melville, J. (2018). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv:1802.03426.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	UMAP (Uniform Manifold Approximation and Projection), uniform manifold approximation and projection, manifold dimension reduction	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Correlati	5	4	4
Sintesi	UMAP (Uniform Manifold Approximation and Projection) is a fast, scalable nonlinear dimension-reduction method grounded in manifold-learning theory, introduced by McInnes, Healy and Melville in 2018. It compresses high-dimensional data into a low-dimensional embedding for visualisation and downstream analysis.	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.