方法对比
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| 在线高斯混合模型× | 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≠ | 2000–2009 | 1967 (formalized 1982) |
| 提出者≠ | Cappé, O. & Moulines, E. (online EM formulation) | MacQueen, J. B.; Lloyd, S. P. |
| 类型≠ | Probabilistic clustering / density estimation (incremental) | Partitional clustering |
| 开创性文献≠ | Cappé, O. & Moulines, E. (2009). On-line expectation-maximization algorithm for latent data models. Journal of the Royal Statistical Society: Series B, 71(3), 593–613. DOI ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ |
| 别名 | Online GMM, Incremental GMM, Streaming Gaussian Mixture Model, Sequential GMM | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means |
| 相关≠ | 5 | 4 |
| 摘要≠ | Online Gaussian Mixture Model adapts the classic GMM to streaming or large-scale data by replacing full-batch EM with incremental updates — processing one observation or mini-batch at a time and continuously refining component means, covariances, and mixing weights without revisiting the entire dataset. | 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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