方法对比
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| 半监督 HDBSCAN× | 半监督高斯混合模型× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2017–present | 2000 |
| 提出者≠ | McInnes, L.; Healy, J. (base HDBSCAN); semi-supervised extensions by various authors | Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T. |
| 类型≠ | Semi-supervised density-based clustering | Generative semi-supervised classifier |
| 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 ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| 别名 | Constrained HDBSCAN, Semi-supervised hierarchical density clustering, HDBSCAN with partial labels, SS-HDBSCAN | SS-GMM, semi-supervised GMM, partially labeled Gaussian mixture model, generative semi-supervised classifier |
| 相关≠ | 6 | 3 |
| 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. | The Semi-supervised Gaussian Mixture Model (SS-GMM) is a generative probabilistic classifier that fits a Gaussian mixture to both labeled and unlabeled data using the Expectation-Maximization algorithm. Labeled points constrain component assignments while unlabeled points improve density estimates, enabling effective learning when annotations are scarce. |
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