Сравнение методов

	DBSCAN	Графовая сеть внимания (Graph Attention Network, GAT)	Графовая нейронная сеть	Иерархическая кластеризация
Область	Машинное обучение	Глубокое обучение	Глубокое обучение	Машинное обучение
Семейство	Machine learning	Machine learning	Machine learning	Machine learning
Год появления	1996	2018	2017	1963
Автор метода	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Veličković, P. et al.	Kipf, T.N. & Welling, M.	Ward, J. H.
Тип	Density-based clustering algorithm	Graph neural network (attention-based)	Deep learning on graph-structured data	Unsupervised clustering (agglomerative)
Основополагающий источник	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.	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.
Другие названия	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network	Grafik Sinir Ağı (GNN), GNN, graph neural net, graph convolutional network	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Связанные	3	4	4	4
Сводка	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).	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.