Comparar métodos

	DBSCAN	Agrupamiento jerárquico	Máquina de Vectores de Soporte (Clasificación)
Campo	Aprendizaje automático	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Machine learning	Machine learning
Año de origen	1996	1963	1995
Autor original	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Ward, J. H.	Cortes, C. & Vapnik, V.
Tipo	Density-based clustering algorithm	Unsupervised clustering (agglomerative)	Maximum-margin classifier (kernel method)
Fuente seminal	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.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
Alias	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
Relacionados	3	4	5
Resumen	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.	Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963.	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.