Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Klasteru analīze× | Lineārā diskriminantā analīze× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Latent structure | Latent structure |
| Izcelsmes gads≠ | 1939–1967 | 1936 |
| Autors≠ | Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means | Ronald A. Fisher |
| Tips≠ | Unsupervised classification / grouping | Supervised classification and dimension reduction |
| Pirmavots≠ | Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913 | Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188. DOI ↗ |
| Citi nosaukumi | clustering, unsupervised classification, data clustering, numerical taxonomy | LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis |
| Saistītās≠ | 5 | 4 |
| 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. | Discriminant analysis finds linear combinations of predictor variables that best separate two or more known groups. It is used both to understand which predictors distinguish the groups and to classify new observations into those groups with minimum error. |
| ScholarGateDatu kopa ↗ |
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