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| Robust Multiple Correspondence Analysis (Robust MCA)× | Clusteranalyse× | |
|---|---|---|
| Fachgebiet | Statistik | Statistik |
| Familie | Latent structure | Latent structure |
| Entstehungsjahr≠ | 2000s | 1939–1967 |
| Urheber≠ | Extensions by Hubert, Rousseeuw and collaborators; building on classical MCA by Benzécri (1973) and Greenacre (1984) | Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means |
| Typ≠ | Robust multivariate dimension reduction | Unsupervised classification / grouping |
| Wegweisende Quelle≠ | Greenacre, M. J. (2017). Correspondence Analysis in Practice (3rd ed.). Chapman & Hall / CRC Press, Boca Raton. ISBN: 978-1498731775 | Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913 |
| Aliasnamen≠ | Robust MCA, Outlier-resistant MCA, Robust HOMALS | clustering, unsupervised classification, data clustering, numerical taxonomy |
| Verwandt≠ | 4 | 5 |
| Zusammenfassung≠ | Robust Multiple Correspondence Analysis extends classical MCA to datasets containing outlying or atypical rows of categorical data. By downweighting influential observations before the singular value decomposition, it produces a low-dimensional map of category relationships that faithfully represents the bulk of the data rather than being distorted by a handful of anomalous cases. | 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. |
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