Methoden vergleichen

	K-means Clustering	Random Forest
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	1967 (formalized 1982)	2001
Urheber	MacQueen, J. B.; Lloyd, S. P.	Breiman, L.
Typ	Partitional clustering	Ensemble (bagging of decision trees)
Wegweisende Quelle	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.
Aliasnamen	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Verwandt	4	4
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.	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.