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	DBSCAN	Rete di Attenzione su Grafo
Campo	Apprendimento automatico	Apprendimento profondo
Famiglia	Machine learning	Machine learning
Anno di origine	1996	2018
Ideatore	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Veličković, P. et al.
Tipo	Density-based clustering algorithm	Graph neural network (attention-based)
Fonte seminale	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.	Veličković, P. et al. (2018). Graph Attention Networks. ICLR.
Alias	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network
Correlati	3	4
Sintesi	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.	The Graph Attention Network (GAT), introduced by Veličković and colleagues in 2018, is a graph neural network variant that learns how much importance to assign to each neighbouring node through a self-attention mechanism. On heterogeneous neighbourhoods and relational classification it produces results superior to graph convolutional networks (GCN).