Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Calinska-Harabaša indekss× | Statistiskā atšķirība (Gap Statistic)× | |
|---|---|---|
| Nozare | Modeļu novērtēšana | Modeļu novērtēšana |
| Saime | MCDM | MCDM |
| Izcelsmes gads≠ | 1974 | 2001 |
| Autors≠ | Tadeusz Calinski, Jerzy Harabasz | Robert Tibshirani, Guenther Walther, Trevor Hastie |
| Tips≠ | Cluster quality metric | Statistical criterion |
| Pirmavots≠ | Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3(1), 1-27. DOI ↗ | Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2), 411-423. DOI ↗ |
| Citi nosaukumi≠ | variance ratio criterion, pseudo F-statistic, CH index | gap index, Tibshirani gap statistic |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | The Calinski-Harabasz Index, also called the Variance Ratio Criterion, was introduced by Calinski and Harabasz in 1974. It is a metric that measures the ratio of between-cluster variance to within-cluster variance, adjusted for the number of clusters and data points. Higher values indicate better-separated, more compact clusters. | The Gap Statistic, developed by Tibshirani, Walther, and Hastie in 2001, is a principled statistical method for determining the optimal number of clusters in a dataset. It compares the observed within-cluster sum of squares to the expected value under a null hypothesis of no clustering structure, providing a theoretically grounded approach to cluster number selection. |
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