Comparer des méthodes

	DBSCAN	Arbre de décision
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1996	1984
Auteur d'origine	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Breiman, Friedman, Olshen & Stone
Type	Density-based clustering algorithm	Recursive partitioning (if-then rules)
Source fondatrice	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.
Alias	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Apparentées	3	5
Résumé	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.