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| Diskriminanzanalyse× | Clusteranalyse× | |
|---|---|---|
| Fachgebiet | Statistik | Statistik |
| Familie | Latent structure | Latent structure |
| Entstehungsjahr≠ | 1936 | 1939–1967 |
| Urheber≠ | Ronald A. Fisher | Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means |
| Typ≠ | Supervised classification and dimension reduction | Unsupervised classification / grouping |
| Wegweisende Quelle≠ | Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188. DOI ↗ | Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913 |
| Aliasnamen | LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis | clustering, unsupervised classification, data clustering, numerical taxonomy |
| Verwandt≠ | 4 | 5 |
| Zusammenfassung≠ | 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. | 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. |
| ScholarGateDatensatz ↗ |
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