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| HDBSCAN× | Enesest juhendav õppimine× | |
|---|---|---|
| Valdkond | Masinõpe | Masinõpe |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 2013 | 2018–2020 |
| Looja≠ | Campello, R. J. G. B.; Moulavi, D.; Sander, J. | LeCun, Y. and community (formalized ~2018–2020) |
| Tüüp≠ | Hierarchical density-based clustering | Representation learning paradigm |
| 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 ↗ | LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/ link ↗ |
| Rööpnimetused | HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN* | SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning |
| Seotud | 3 | 3 |
| 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. | Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples. |
| ScholarGateAndmestik ↗ |
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