Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Inertie× | Indice de Dunn× | |
|---|---|---|
| Domaine | Évaluation de modèles | Évaluation de modèles |
| Famille | MCDM | MCDM |
| Année d'origine≠ | 1967 | 1974 |
| Auteur d'origine≠ | Stuart Lloyd, James MacQueen | Joseph C. Dunn |
| Type≠ | Clustering quality metric | Cluster quality metric |
| Source fondatrice≠ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137. DOI ↗ | Dunn, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4(1), 95-104. DOI ↗ |
| Alias≠ | WCSS, within-cluster sum of squares, cluster cohesion | Dunn's index, separation coefficient |
| Apparentées | 5 | 5 |
| Résumé≠ | Inertia, also called Within-Cluster Sum of Squares (WCSS), is a measure of cluster cohesion that quantifies how tightly points are grouped around their cluster centroids. Lower values indicate more compact, cohesive clusters. Inertia is the primary objective function for k-means clustering and has been a fundamental metric since the method's introduction. | 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. |
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