Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Índice de Davies-Bouldin× | Índice de Dunn× | |
|---|---|---|
| Área | Avaliação de modelos | Avaliação de modelos |
| Família | MCDM | MCDM |
| Ano de origem≠ | 1979 | 1974 |
| Autor original≠ | David L. Davies, Donald W. Bouldin | Joseph C. Dunn |
| Tipo | Cluster quality metric | Cluster quality metric |
| Fonte 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 ↗ | Dunn, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4(1), 95-104. DOI ↗ |
| Outros nomes | DBI, Davies Bouldin index | Dunn's index, separation coefficient |
| Relacionados | 5 | 5 |
| Resumo≠ | 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 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. |
| ScholarGateConjunto de dados ↗ |
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