Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Indice de Calinski-Harabasz× | Méthode du coude× | |
|---|---|---|
| Domaine | Évaluation de modèles | Évaluation de modèles |
| Famille | MCDM | MCDM |
| Année d'origine≠ | 1974 | 1953 |
| Auteur d'origine≠ | Tadeusz Calinski, Jerzy Harabasz | Robert Thorndike |
| Type≠ | Cluster quality metric | Heuristic optimization criterion |
| Source fondatrice≠ | Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3(1), 1-27. DOI ↗ | Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics. link ↗ |
| Alias≠ | variance ratio criterion, pseudo F-statistic, CH index | elbow analysis, knee detection |
| Apparentées | 5 | 5 |
| Résumé≠ | 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 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 ↗ |
|
|