Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Кластеризація методом k-середніх× | Регуляризована Гауссова Суміш Моделей× | |
|---|---|---|
| Галузь | Машинне навчання | Машинне навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 1967 (formalized 1982) | 2000s–2010s |
| Автор методу≠ | MacQueen, J. B.; Lloyd, S. P. | Fraley, C. & Raftery, A. E. (regularization formalized); sklearn team (practical reg_covar parameter) |
| Тип≠ | Partitional clustering | Probabilistic clustering with regularization |
| Основоположне джерело≠ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ | Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631. DOI ↗ |
| Інші назви | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means | Regularized GMM, GMM with covariance regularization, stabilized Gaussian mixture model, penalized GMM |
| Пов'язані≠ | 4 | 5 |
| Підсумок≠ | 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. | A Regularized Gaussian Mixture Model (GMM) adds a small positive constant to the diagonal of each component covariance matrix during the Expectation-Maximization algorithm, preventing singular or near-singular matrices that cause numerical failures when the data are sparse, high-dimensional, or contain near-duplicate observations. |
| ScholarGateНабір даних ↗ |
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