Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Онлайновая гауссова смесь (Online Gaussian Mixture Model)× | Кластеризация методом k-средних× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2000–2009 | 1967 (formalized 1982) |
| Автор метода≠ | Cappé, O. & Moulines, E. (online EM formulation) | MacQueen, J. B.; Lloyd, S. P. |
| Тип≠ | Probabilistic clustering / density estimation (incremental) | Partitional clustering |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия | Online GMM, Incremental GMM, Streaming Gaussian Mixture Model, Sequential GMM | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means |
| Связанные≠ | 5 | 4 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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