方法对比
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| 在线高斯混合模型× | 在线K均值聚类 (Online K-means)× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2000–2009 | 1967 (online update rule); 2010 (mini-batch variant) |
| 提出者≠ | Cappé, O. & Moulines, E. (online EM formulation) | MacQueen, J. (batch); Sculley, D. (mini-batch web-scale variant) |
| 类型≠ | Probabilistic clustering / density estimation (incremental) | Unsupervised clustering (online/streaming) |
| 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 ↗ | MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, pp. 281–297. University of California Press. link ↗ |
| 别名 | Online GMM, Incremental GMM, Streaming Gaussian Mixture Model, Sequential GMM | sequential k-means, streaming k-means, incremental k-means, online clustering |
| 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. | Online K-means is a streaming variant of the classical K-means algorithm that updates cluster centroids one observation at a time — or in small mini-batches — without storing the entire dataset in memory. It is particularly suited to large-scale, real-time, or continuously arriving data where batch recomputation would be too slow or impractical. |
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