Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Кластеризація методом k-середніх× | Напівкерований HDBSCAN× | |
|---|---|---|
| Галузь | Машинне навчання | Машинне навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 1967 (formalized 1982) | 2017–present |
| Автор методу≠ | 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 |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви | 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 |
| Пов'язані≠ | 4 | 6 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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