Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Semi-supervised K-means× | DBSCAN× | |
|---|---|---|
| Vakgebied | Machine learning | Machine learning |
| Familie | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 2001–2002 | 1996 |
| Grondlegger≠ | Wagstaff, K. et al. (constrained); Basu, S. et al. (seeded) | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. |
| Type≠ | Semi-supervised clustering | Density-based clustering algorithm |
| Oorspronkelijke bron≠ | Wagstaff, K., Cardie, C., Rogers, S., & Schroedl, S. (2001). Constrained K-means Clustering with Background Knowledge. In Proceedings of the 18th International Conference on Machine Learning (ICML 2001), pp. 577–584. link ↗ | 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 ↗ |
| Aliassen≠ | constrained K-means, seeded K-means, partially supervised K-means, SS-K-means | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering |
| Verwant≠ | 5 | 3 |
| Samenvatting≠ | Semi-supervised K-means extends standard K-means clustering by incorporating partial supervision — either a small set of labeled seed points or pairwise must-link and cannot-link constraints — to guide cluster formation. It bridges unsupervised clustering and fully supervised classification, enabling more meaningful clusters when labels are scarce but costly to obtain in full. | 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. |
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