Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| UMAP× | Agrupamiento K-medias× | |
|---|---|---|
| Campo | Aprendizaje automático | Aprendizaje automático |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2018 | 1967 (formalized 1982) |
| Autor original≠ | McInnes, L.; Healy, J.; Melville, J. | MacQueen, J. B.; Lloyd, S. P. |
| Tipo≠ | Nonlinear manifold-learning dimension reduction | Partitional clustering |
| Fuente seminal≠ | McInnes, L., Healy, J. & Melville, J. (2018). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv:1802.03426. link ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ |
| Alias≠ | UMAP (Uniform Manifold Approximation and Projection), uniform manifold approximation and projection, manifold dimension reduction | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means |
| Relacionados≠ | 5 | 4 |
| Resumen≠ | UMAP (Uniform Manifold Approximation and Projection) is a fast, scalable nonlinear dimension-reduction method grounded in manifold-learning theory, introduced by McInnes, Healy and Melville in 2018. It compresses high-dimensional data into a low-dimensional embedding for visualisation and downstream analysis. | 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. |
| ScholarGateConjunto de datos ↗ |
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