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| HDBSCAN× | DBSCAN× | |
|---|---|---|
| Fagområde | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 2013 | 1996 |
| Ophavsperson≠ | Campello, R. J. G. B.; Moulavi, D.; Sander, J. | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. |
| Type≠ | Hierarchical density-based clustering | Density-based clustering algorithm |
| Oprindelig kilde≠ | 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 ↗ | 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 ↗ |
| Aliasser≠ | HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN* | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering |
| Relaterede | 3 | 3 |
| Resumé≠ | 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. | 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. |
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