Comparar métodos

	Agrupamento K-Means	Random Forest
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	1967	2001
Autor original	MacQueen, J.	Breiman, L.
Tipo	Partitional clustering (centroid-based)	Ensemble (bagging of decision trees)
Fonte seminal	MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Outros nomes	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Relacionados	3	4
Resumo	K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory 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.