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| Regularyzowany model mieszaniny Gaussa× | Grupowanie K-średnich (K-means Clustering)× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2000s–2010s | 1967 (formalized 1982) |
| Twórca≠ | Fraley, C. & Raftery, A. E. (regularization formalized); sklearn team (practical reg_covar parameter) | MacQueen, J. B.; Lloyd, S. P. |
| Typ≠ | Probabilistic clustering with regularization | Partitional clustering |
| Źródło pierwotne≠ | 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 ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ |
| Inne nazwy | Regularized GMM, GMM with covariance regularization, stabilized Gaussian mixture model, penalized GMM | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means |
| Pokrewne≠ | 5 | 4 |
| Podsumowanie≠ | 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. | 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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