Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Робастная иерархическая кластеризация× | Иерархическая кластеризация× | |
|---|---|---|
| Область≠ | Статистика | Машинное обучение |
| Семейство≠ | Latent structure | Machine learning |
| Год появления≠ | 1990 | 1963 |
| Автор метода≠ | Kaufman & Rousseeuw (building on Ward, 1963 and others) | Ward, J. H. |
| Тип≠ | Robust unsupervised clustering | Unsupervised clustering (agglomerative) |
| Основополагающий источник≠ | Kaufman, L. & Rousseeuw, P. J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley. ISBN: 978-0471878766 | Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244. DOI ↗ |
| Другие названия≠ | robust agglomerative clustering, outlier-resistant hierarchical clustering, robust linkage clustering, RHC | Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering |
| Связанные≠ | 5 | 4 |
| Сводка≠ | Robust hierarchical clustering extends classical agglomerative or divisive hierarchical clustering by replacing sensitive distance measures and linkage criteria with outlier-resistant alternatives, preserving cluster structure even when data contain anomalous observations or heavy-tailed distributions. | 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. |
| ScholarGateНабор данных ↗ |
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