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| Robuste Clusteranalyse (TCLUST)× | Robuste Diskriminanzanalyse× | |
|---|---|---|
| Fachgebiet | Statistik | Statistik |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 2008 | 1997 |
| Urheber≠ | García-Escudero, Gordaliza, Matrán & Mayo-Iscar (TCLUST) | Hawkins & McLachlan (high-breakdown LDA); Croux & Dehon (S-estimator robust LDA) |
| Typ≠ | Robust model-based clustering | Robust classification / discriminant analysis |
| Wegweisende Quelle≠ | García-Escudero, L. A., Gordaliza, A., Matrán, C., & Mayo-Iscar, A. (2008). A General Trimming Approach to Robust Cluster Analysis. The Annals of Statistics, 36(3), 1324-1345. DOI ↗ | Hawkins, D. M. & McLachlan, G. J. (1997). High Breakdown Linear Discriminant Analysis. Journal of the American Statistical Association, 92(437), 136-143. DOI ↗ |
| Aliasnamen | TCLUST, trimmed clustering, robust clustering, Robust Küme Analizi (TCLUST) | robust LDA, high-breakdown discriminant analysis, MCD-based discriminant analysis, Robust Diskriminant Analizi |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | Robust Cluster Analysis is a trimmed model-based clustering method, introduced by García-Escudero and colleagues in 2008, that partitions continuous multivariate data into clusters while resisting the influence of outliers and noise. By setting aside a fraction of the most discordant observations, it keeps the recovered cluster structure from being contaminated by stray points. | Robust Discriminant Analysis is a classification method that separates groups with a linear discriminant function while resisting the influence of outliers. It replaces the classical mean and covariance with a high-breakdown estimator such as the Minimum Covariance Determinant (MCD), an approach developed by Hawkins & McLachlan (1997) and Croux & Dehon (2001). |
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