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| HDBSCAN× | Veebipõhine õpe× | |
|---|---|---|
| Valdkond | Masinõpe | Masinõpe |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 2013 | 1958–2000s |
| Looja≠ | Campello, R. J. G. B.; Moulavi, D.; Sander, J. | Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors) |
| Tüüp≠ | Hierarchical density-based clustering | Learning paradigm (sequential model update) |
| Algallikas≠ | 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 ↗ | Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. DOI ↗ |
| Rööpnimetused | HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN* | incremental learning, sequential learning, streaming learning, online machine learning |
| Seotud≠ | 3 | 6 |
| Kokkuvõte≠ | 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 learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight. |
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