Compară metode

	DBSCAN	Pădurea Aleatoare (Random Forest)
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	1996	2001
Autorul original	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Breiman, L.
Tip	Density-based clustering algorithm	Ensemble (bagging of decision trees)
Sursa seminală	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Denumiri alternative	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Înrudite	3	4
Rezumat	DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes.	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.