Usporedite metode

	DBSCAN	Grafne neuronske mreže	Hijerarhijsko grupiranje
Područje	Strojno učenje	Duboko učenje	Strojno učenje
Obitelj	Machine learning	Machine learning	Machine learning
Godina nastanka	1996	2017	1963
Tvorac	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Kipf, T.N. & Welling, M.	Ward, J. H.
Vrsta	Density-based clustering algorithm	Deep learning on graph-structured data	Unsupervised clustering (agglomerative)
Temeljni izvor	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.	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. ICLR.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.
Drugi nazivi	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	Grafik Sinir Ağı (GNN), GNN, graph neural net, graph convolutional network	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Srodne	3	4	4
Sažetak	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 Graph Neural Network (GNN) is a deep learning method, popularised by Kipf and Welling in 2017 with the Graph Convolutional Network, that learns from the relationships in network (graph) structures made of nodes and edges. It is designed for data that is naturally relational, such as social networks, molecular structures, and recommendation systems.	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.