Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Онлайнова гаусова суміш (Online Gaussian Mixture Model)× | Online K-means× | |
|---|---|---|
| Галузь | Машинне навчання | Машинне навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 2000–2009 | 1967 (online update rule); 2010 (mini-batch variant) |
| Автор методу≠ | Cappé, O. & Moulines, E. (online EM formulation) | MacQueen, J. (batch); Sculley, D. (mini-batch web-scale variant) |
| Тип≠ | Probabilistic clustering / density estimation (incremental) | Unsupervised clustering (online/streaming) |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви | Online GMM, Incremental GMM, Streaming Gaussian Mixture Model, Sequential GMM | sequential k-means, streaming k-means, incremental k-means, online clustering |
| Пов'язані≠ | 5 | 4 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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