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| K-means клъстеризация× | Полу-наблюдавано HDBSCAN× | |
|---|---|---|
| Област | Машинно обучение | Машинно обучение |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 1967 (formalized 1982) | 2017–present |
| Създател≠ | MacQueen, J. B.; Lloyd, S. P. | McInnes, L.; Healy, J. (base HDBSCAN); semi-supervised extensions by various authors |
| Тип≠ | Partitional clustering | Semi-supervised density-based clustering |
| Основополагащ източник≠ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ | McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205. DOI ↗ |
| Други названия | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means | Constrained HDBSCAN, Semi-supervised hierarchical density clustering, HDBSCAN with partial labels, SS-HDBSCAN |
| Свързани≠ | 4 | 6 |
| Резюме≠ | 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. | Semi-supervised HDBSCAN extends the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm by incorporating partial supervision — such as must-link and cannot-link pairwise constraints or a small set of labeled examples — to guide the density-based cluster hierarchy toward cluster assignments that are consistent with available domain knowledge. |
| ScholarGateНабор от данни ↗ |
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