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| Indeks Daviesa-Bouldina× | Skorygowany indeks Randą× | |
|---|---|---|
| Dziedzina | Ocena modeli | Ocena modeli |
| Rodzina | MCDM | MCDM |
| Rok powstania≠ | 1979 | 1985 |
| Twórca≠ | David L. Davies, Donald W. Bouldin | Lawrence Hubert, Phipps Arabie |
| Typ≠ | Cluster quality metric | External similarity metric |
| Źródło pierwotne≠ | Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2), 224-227. DOI ↗ | Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2(1), 193-218. DOI ↗ |
| Inne nazwy | DBI, Davies Bouldin index | ARI, adjusted Rand coefficient |
| Pokrewne | 5 | 5 |
| Podsumowanie≠ | 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 Adjusted Rand Index (ARI), developed by Hubert and Arabie in 1985, is an external clustering evaluation metric that measures the agreement between a predicted clustering and a ground truth labeling. It ranges from -1 to 1, where 1 indicates perfect agreement, 0 indicates random clustering, and negative values indicate performance worse than random chance. |
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