方法对比
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| 半监督 HDBSCAN× | K-means聚类× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2017–present | 1967 (formalized 1982) |
| 提出者≠ | McInnes, L.; Healy, J. (base HDBSCAN); semi-supervised extensions by various authors | MacQueen, J. B.; Lloyd, S. P. |
| 类型≠ | Semi-supervised density-based clustering | Partitional clustering |
| 开创性文献≠ | McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205. DOI ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ |
| 别名 | Constrained HDBSCAN, Semi-supervised hierarchical density clustering, HDBSCAN with partial labels, SS-HDBSCAN | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means |
| 相关≠ | 6 | 4 |
| 摘要≠ | 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. | 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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