Compara mètodes
Revisa els mètodes seleccionats l'un al costat de l'altre; les files que difereixen es ressalten.
| DBSCAN× | Graph Attention Network× | Xarxa Neuronal de Grafs× | |
|---|---|---|---|
| Camp≠ | Aprenentatge automàtic | Aprenentatge profund | Aprenentatge profund |
| Família | Machine learning | Machine learning | Machine learning |
| Any d'origen≠ | 1996 | 2018 | 2017 |
| Autor original≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | Veličković, P. et al. | Kipf, T.N. & Welling, M. |
| Tipus≠ | Density-based clustering algorithm | Graph neural network (attention-based) | Deep learning on graph-structured data |
| Font 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. link ↗ | Veličković, P. et al. (2018). Graph Attention Networks. ICLR. link ↗ | Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. ICLR. link ↗ |
| Àlies≠ | 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 |
| Relacionats≠ | 3 | 4 | 4 |
| Resum≠ | 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. |
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