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| Índice Davies-Bouldin× | Inercia× | |
|---|---|---|
| Campo | Evaluación de modelos | Evaluación de modelos |
| Familia | MCDM | MCDM |
| Año de origen≠ | 1979 | 1967 |
| Autor original≠ | David L. Davies, Donald W. Bouldin | Stuart Lloyd, James MacQueen |
| Tipo≠ | Cluster quality metric | Clustering quality metric |
| Fuente seminal≠ | Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2), 224-227. DOI ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137. DOI ↗ |
| Alias≠ | DBI, Davies Bouldin index | WCSS, within-cluster sum of squares, cluster cohesion |
| Relacionados | 5 | 5 |
| Resumen≠ | 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. | 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. |
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