方法对比
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| 半监督K-均值× | 谱聚类× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2001–2002 | 2002 |
| 提出者≠ | Wagstaff, K. et al. (constrained); Basu, S. et al. (seeded) | Ng, A. Y.; Jordan, M. I.; Weiss, Y. |
| 类型≠ | Semi-supervised clustering | Graph-based clustering (spectral method) |
| 开创性文献≠ | Wagstaff, K., Cardie, C., Rogers, S., & Schroedl, S. (2001). Constrained K-means Clustering with Background Knowledge. In Proceedings of the 18th International Conference on Machine Learning (ICML 2001), pp. 577–584. link ↗ | Ng, A. Y., Jordan, M. I., & Weiss, Y. (2002). On Spectral Clustering: Analysis and an Algorithm. Advances in Neural Information Processing Systems, 14, 849–856. link ↗ |
| 别名≠ | constrained K-means, seeded K-means, partially supervised K-means, SS-K-means | NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering |
| 相关 | 5 | 5 |
| 摘要≠ | Semi-supervised K-means extends standard K-means clustering by incorporating partial supervision — either a small set of labeled seed points or pairwise must-link and cannot-link constraints — to guide cluster formation. It bridges unsupervised clustering and fully supervised classification, enabling more meaningful clusters when labels are scarce but costly to obtain in full. | Spectral Clustering is a graph-based unsupervised learning algorithm, formalized by Ng, Jordan, and Weiss in 2002, that maps data points into a low-dimensional eigenspace derived from the similarity graph's Laplacian before applying k-means. This spectral embedding makes it possible to recover clusters of arbitrary shape — rings, crescents, interleaved spirals — that Euclidean distance-based methods consistently fail to separate. |
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