Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Índice de Dunn× | Índice Calinski-Harabasz× | |
|---|---|---|
| Campo | Evaluación de modelos | Evaluación de modelos |
| Familia | MCDM | MCDM |
| Año de origen | 1974 | 1974 |
| Autor original≠ | Joseph C. Dunn | Tadeusz Calinski, Jerzy Harabasz |
| Tipo | Cluster quality metric | Cluster quality metric |
| Fuente seminal≠ | Dunn, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4(1), 95-104. DOI ↗ | Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3(1), 1-27. DOI ↗ |
| Alias≠ | Dunn's index, separation coefficient | variance ratio criterion, pseudo F-statistic, CH index |
| Relacionados | 5 | 5 |
| Resumen≠ | The Dunn Index, introduced by Joseph C. Dunn in 1974, is a metric that captures cluster quality by measuring the ratio of the minimum between-cluster distance to the maximum within-cluster diameter. Higher values indicate well-separated and compact clusters, with better clustering quality. | 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. |
| ScholarGateConjunto de datos ↗ |
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