Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Ієрархічна кластеризація× | Лінійний дискримінантний аналіз (LDA× | |
|---|---|---|
| Галузь≠ | Машинне навчання | Статистика |
| Родина≠ | Machine learning | Hypothesis test |
| Рік появи≠ | 1963 | 1936 |
| Автор методу≠ | Ward, J. H. | Ronald A. Fisher |
| Тип≠ | Unsupervised clustering (agglomerative) | Parametric linear classifier / dimensionality reduction |
| Основоположне джерело≠ | Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244. DOI ↗ | Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188. DOI ↗ |
| Інші назви | Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering | LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis |
| Пов'язані≠ | 4 | 7 |
| Підсумок≠ | 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. | Linear Discriminant Analysis (LDA) is a parametric supervised classification method that finds the linear combination of continuous predictors that best separates two or more predefined groups. Introduced by Ronald A. Fisher in his landmark 1936 paper on taxonomic measurements, it simultaneously serves as a classifier and a dimensionality-reduction tool, and can be understood as the classification-oriented counterpart of MANOVA. |
| ScholarGateНабір даних ↗ |
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