Compară metode

	DBSCAN	Arbore de decizie
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	1996	1984
Autorul original	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Breiman, Friedman, Olshen & Stone
Tip	Density-based clustering algorithm	Recursive partitioning (if-then rules)
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., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Denumiri alternative	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Înrudite	3	5
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.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.