Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Кластерный анализ× | Многомерное шкалирование (MDS)× | |
|---|---|---|
| Область | Статистика | Статистика |
| Семейство | Latent structure | Latent structure |
| Год появления≠ | 1939–1967 | 1952–1964 |
| Автор метода≠ | Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means | Warren S. Torgerson (metric MDS, 1952); Joseph B. Kruskal (non-metric MDS, 1964) |
| Тип≠ | Unsupervised classification / grouping | Dimensionality reduction / visualization |
| Основополагающий источник≠ | Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913 | Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29(1), 1–27. DOI ↗ |
| Другие названия | clustering, unsupervised classification, data clustering, numerical taxonomy | MDS, metric MDS, non-metric MDS, proximity scaling |
| Связанные | 5 | 5 |
| Сводка≠ | Cluster analysis is a family of unsupervised multivariate techniques that partition a set of objects or observations into internally homogeneous, mutually distinct groups — clusters — based on measured characteristics, without any prior knowledge of group membership. It is widely used in market segmentation, bioinformatics, psychology, and social science to reveal natural groupings in data. | Multidimensional scaling maps objects described only by pairwise similarities or dissimilarities into a low-dimensional geometric space so that distances in that space reflect the original proximity structure as faithfully as possible. It is widely used to visualize the hidden structure of psychological, social, and behavioral data. |
| ScholarGateНабор данных ↗ |
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