Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Indice de Dunn× | Méthode du coude× | |
|---|---|---|
| Domaine | Évaluation de modèles | Évaluation de modèles |
| Famille | MCDM | MCDM |
| Année d'origine≠ | 1974 | 1953 |
| Auteur d'origine≠ | Joseph C. Dunn | Robert Thorndike |
| Type≠ | Cluster quality metric | Heuristic optimization criterion |
| Source fondatrice≠ | Dunn, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4(1), 95-104. DOI ↗ | Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics. link ↗ |
| Alias | Dunn's index, separation coefficient | elbow analysis, knee detection |
| Apparentées | 5 | 5 |
| Résumé≠ | 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 Elbow Method is a heuristic for selecting the optimal number of clusters in partitional clustering. Introduced by Robert Thorndike in 1953, it involves fitting clustering models for increasing numbers of clusters and plotting the within-cluster sum of squares (WCSS) against the number of clusters. The 'elbow' occurs where the rate of WCSS decrease sharply changes, suggesting an optimal cluster count. |
| ScholarGateJeu de données ↗ |
|
|