Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Semi-supervised HDBSCAN× | DBSCAN× | |
|---|---|---|
| Vakgebied | Machine learning | Machine learning |
| Familie | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 2017–present | 1996 |
| Grondlegger≠ | McInnes, L.; Healy, J. (base HDBSCAN); semi-supervised extensions by various authors | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. |
| Type≠ | Semi-supervised density-based clustering | Density-based clustering algorithm |
| Oorspronkelijke bron≠ | McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205. DOI ↗ | 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 HDBSCAN, Semi-supervised hierarchical density clustering, HDBSCAN with partial labels, SS-HDBSCAN | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering |
| Verwant≠ | 6 | 3 |
| Samenvatting≠ | Semi-supervised HDBSCAN extends the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm by incorporating partial supervision — such as must-link and cannot-link pairwise constraints or a small set of labeled examples — to guide the density-based cluster hierarchy toward cluster assignments that are consistent with available domain knowledge. | 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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