Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Indice de Davies-Bouldin× | Indice de Rand Ajusté× | |
|---|---|---|
| Domaine | Évaluation de modèles | Évaluation de modèles |
| Famille | MCDM | MCDM |
| Année d'origine≠ | 1979 | 1985 |
| Auteur d'origine≠ | David L. Davies, Donald W. Bouldin | Lawrence Hubert, Phipps Arabie |
| Type≠ | Cluster quality metric | External similarity metric |
| Source fondatrice≠ | Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2), 224-227. DOI ↗ | Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2(1), 193-218. DOI ↗ |
| Alias | DBI, Davies Bouldin index | ARI, adjusted Rand coefficient |
| Apparentées | 5 | 5 |
| Résumé≠ | 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. | The Adjusted Rand Index (ARI), developed by Hubert and Arabie in 1985, is an external clustering evaluation metric that measures the agreement between a predicted clustering and a ground truth labeling. It ranges from -1 to 1, where 1 indicates perfect agreement, 0 indicates random clustering, and negative values indicate performance worse than random chance. |
| ScholarGateJeu de données ↗ |
|
|