Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Klasteru analīze× | Multidimensionālā skalēšana (MDS)× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Latent structure | Latent structure |
| Izcelsmes gads≠ | 1939–1967 | 1952–1964 |
| Autors≠ | 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) |
| Tips≠ | Unsupervised classification / grouping | Dimensionality reduction / visualization |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi | clustering, unsupervised classification, data clustering, numerical taxonomy | MDS, metric MDS, non-metric MDS, proximity scaling |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. |
| ScholarGateDatu kopa ↗ |
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