方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| K-means聚类× | 半监督学习× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1967 (formalized 1982) | 1970s–2006 (formalized) |
| 提出者≠ | MacQueen, J. B.; Lloyd, S. P. | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| 类型≠ | Partitional clustering | Learning paradigm |
| 开创性文献≠ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| 别名 | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| 相关≠ | 4 | 5 |
| 摘要≠ | 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 learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained. |
| ScholarGate数据集 ↗ |
|
|