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| HDBSCAN× | Clustering K-means× | |
|---|---|---|
| Campo | Apprendimento automatico | Apprendimento automatico |
| Famiglia | Machine learning | Machine learning |
| Anno di origine≠ | 2013 | 1967 (formalized 1982) |
| Ideatore≠ | Campello, R. J. G. B.; Moulavi, D.; Sander, J. | MacQueen, J. B.; Lloyd, S. P. |
| Tipo≠ | Hierarchical density-based clustering | Partitional clustering |
| Fonte seminale≠ | 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 ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ |
| Alias | HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN* | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means |
| Correlati≠ | 3 | 4 |
| Sintesi≠ | 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. | K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis. |
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