Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Statistika mezery (Gap Statistic)× | Calinski-Harabaszův index× | Setrvačnost× | |
|---|---|---|---|
| Obor | Hodnocení modelů | Hodnocení modelů | Hodnocení modelů |
| Rodina | MCDM | MCDM | MCDM |
| Rok vzniku≠ | 2001 | 1974 | 1967 |
| Tvůrce≠ | Robert Tibshirani, Guenther Walther, Trevor Hastie | Tadeusz Calinski, Jerzy Harabasz | Stuart Lloyd, James MacQueen |
| Typ≠ | Statistical criterion | Cluster quality metric | Clustering quality metric |
| Původní zdroj≠ | 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 ↗ | Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3(1), 1-27. DOI ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137. DOI ↗ |
| Další názvy≠ | gap index, Tibshirani gap statistic | variance ratio criterion, pseudo F-statistic, CH index | WCSS, within-cluster sum of squares, cluster cohesion |
| Příbuzné | 5 | 5 | 5 |
| Shrnutí≠ | 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. | 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. | Inertia, also called Within-Cluster Sum of Squares (WCSS), is a measure of cluster cohesion that quantifies how tightly points are grouped around their cluster centroids. Lower values indicate more compact, cohesive clusters. Inertia is the primary objective function for k-means clustering and has been a fundamental metric since the method's introduction. |
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