Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Análisis de Componentes Principales× | Agrupamiento jerárquico× | |
|---|---|---|
| Campo | Aprendizaje automático | Aprendizaje automático |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2002 | 1963 |
| Autor original≠ | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) | Ward, J. H. |
| Tipo≠ | Unsupervised dimensionality reduction | Unsupervised clustering (agglomerative) |
| Fuente seminal≠ | Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI ↗ | Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244. DOI ↗ |
| Alias≠ | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform | Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering |
| Relacionados≠ | 3 | 4 |
| Resumen≠ | Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures. | Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963. |
| ScholarGateConjunto de datos ↗ |
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