Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Напівкерований HDBSCAN× | DBSCAN× | |
|---|---|---|
| Галузь | Машинне навчання | Машинне навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 2017–present | 1996 |
| Автор методу≠ | McInnes, L.; Healy, J. (base HDBSCAN); semi-supervised extensions by various authors | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. |
| Тип≠ | Semi-supervised density-based clustering | Density-based clustering algorithm |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви≠ | Constrained HDBSCAN, Semi-supervised hierarchical density clustering, HDBSCAN with partial labels, SS-HDBSCAN | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering |
| Пов'язані≠ | 6 | 3 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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