Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| DBSCAN× | Graafiverkko (Graph Attention Network, GAT)× | |
|---|---|---|
| Tieteenala≠ | Koneoppiminen | Syväoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 1996 | 2018 |
| Kehittäjä≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | Veličković, P. et al. |
| Tyyppi≠ | Density-based clustering algorithm | Graph neural network (attention-based) |
| Alkuperäislähde≠ | 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 ↗ |
| Rinnakkaisnimet≠ | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network |
| Liittyvät≠ | 3 | 4 |
| Tiivistelmä≠ | 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). |
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