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| DBSCAN× | HDBSCAN× | Online'owy model mieszaniny Gaussa× | |
|---|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning | Machine learning |
| Rok powstania≠ | 1996 | 2013 | 2000–2009 |
| Twórca≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | Campello, R. J. G. B.; Moulavi, D.; Sander, J. | Cappé, O. & Moulines, E. (online EM formulation) |
| Typ≠ | Density-based clustering algorithm | Hierarchical density-based clustering | Probabilistic clustering / density estimation (incremental) |
| Źródło pierwotne≠ | 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 ↗ | Campello, R. J. G. B., Moulavi, D., & Sander, J. (2013). Density-Based Clustering Based on Hierarchical Density Estimates. In J. Pei et al. (Eds.), Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science, vol. 7819 (pp. 160–172). Springer, Berlin, Heidelberg. DOI ↗ | Cappé, O. & Moulines, E. (2009). On-line expectation-maximization algorithm for latent data models. Journal of the Royal Statistical Society: Series B, 71(3), 593–613. DOI ↗ |
| Inne nazwy≠ | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN* | Online GMM, Incremental GMM, Streaming Gaussian Mixture Model, Sequential GMM |
| Pokrewne≠ | 3 | 3 | 5 |
| Podsumowanie≠ | 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. | HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm introduced by Campello, Moulavi, and Sander in 2013. It extends DBSCAN by building a full hierarchy of density-based clusters across all density scales and then extracting a stable flat partition, making it robust to datasets where cluster densities vary substantially across regions. | Online Gaussian Mixture Model adapts the classic GMM to streaming or large-scale data by replacing full-batch EM with incremental updates — processing one observation or mini-batch at a time and continuously refining component means, covariances, and mixing weights without revisiting the entire dataset. |
| ScholarGateZbiór danych ↗ |
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