Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| DBSCAN× | Graph Attention Network× | Hiërarchische clustering× | |
|---|---|---|---|
| Vakgebied≠ | Machine learning | Deep learning | Machine learning |
| Familie | Machine learning | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 1996 | 2018 | 1963 |
| Grondlegger≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | Veličković, P. et al. | Ward, J. H. |
| Type≠ | Density-based clustering algorithm | Graph neural network (attention-based) | Unsupervised clustering (agglomerative) |
| Oorspronkelijke bron≠ | 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 ↗ | Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244. DOI ↗ |
| Aliassen≠ | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network | Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering |
| Verwant≠ | 3 | 4 | 4 |
| Samenvatting≠ | 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). | 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. |
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