Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| DBSCAN× | Ensemble HDBSCAN× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1996 | 2011–2017 |
| Autors≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | Vega-Pons, S. & Ruiz-Shulcloper, J. (ensemble clustering framework); McInnes, L. et al. (HDBSCAN base) |
| Tips≠ | Density-based clustering algorithm | Consensus clustering ensemble |
| Pirmavots≠ | 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 ↗ | McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205. DOI ↗ |
| Citi nosaukumi≠ | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | HDBSCAN ensemble clustering, consensus HDBSCAN, multi-run HDBSCAN, cluster ensemble HDBSCAN |
| Saistītās≠ | 3 | 4 |
| Kopsavilkums≠ | 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. | Ensemble HDBSCAN runs HDBSCAN multiple times under different hyperparameter settings or data subsamples and combines the resulting partitions into a single stable consensus clustering. Because HDBSCAN is sensitive to its minimum cluster size and minimum samples parameters, pooling multiple runs greatly reduces sensitivity to any single configuration and yields more reproducible cluster assignments on noisy, high-dimensional data. |
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