Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Índice Calinski-Harabasz× | Índice Davies-Bouldin× | |
|---|---|---|
| Campo | Evaluación de modelos | Evaluación de modelos |
| Familia | MCDM | MCDM |
| Año de origen≠ | 1974 | 1979 |
| Autor original≠ | Tadeusz Calinski, Jerzy Harabasz | David L. Davies, Donald W. Bouldin |
| Tipo | Cluster quality metric | Cluster quality metric |
| Fuente seminal≠ | Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3(1), 1-27. DOI ↗ | Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2), 224-227. DOI ↗ |
| Alias≠ | variance ratio criterion, pseudo F-statistic, CH index | DBI, Davies Bouldin index |
| Relacionados | 5 | 5 |
| Resumen≠ | 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 Davies-Bouldin Index, introduced by Davies and Bouldin in 1979, is a metric for evaluating clustering quality based on the average similarity between each cluster and its most similar neighboring cluster. Lower values indicate better clustering, with a minimum of 0 representing perfectly separated, non-overlapping clusters. |
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