Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Robusta hierarhiskā klasterēšana× | Robust K-means Clustering× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Latent structure | Latent structure |
| Izcelsmes gads≠ | 1990 | 1997 |
| Autors≠ | Kaufman & Rousseeuw (building on Ward, 1963 and others) | Cuesta-Albertos, Gordaliza & Matrán |
| Tips≠ | Robust unsupervised clustering | Robust partitional clustering |
| Pirmavots≠ | Kaufman, L. & Rousseeuw, P. J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley. ISBN: 978-0471878766 | Cuesta-Albertos, J. A., Gordaliza, A., & Matrán, C. (1997). Trimmed k-means: An attempt to robustify quantizers. The Annals of Statistics, 25(2), 553–576. DOI ↗ |
| Citi nosaukumi | robust agglomerative clustering, outlier-resistant hierarchical clustering, robust linkage clustering, RHC | trimmed k-means, TCLUST k-means, contamination-resistant k-means, outlier-robust clustering |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | Robust hierarchical clustering extends classical agglomerative or divisive hierarchical clustering by replacing sensitive distance measures and linkage criteria with outlier-resistant alternatives, preserving cluster structure even when data contain anomalous observations or heavy-tailed distributions. | Robust K-means clustering is an extension of classical k-means that protects cluster estimates from distortion caused by outliers or contaminated observations. By trimming a user-specified fraction of the most extreme points before updating cluster centers, the algorithm yields stable, meaningful partitions even when the data contain atypical cases that would severely bias standard k-means. |
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