Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Normalizovaná vzájemná informace× | Daviesův-Bouldinův index× | |
|---|---|---|
| Obor | Hodnocení modelů | Hodnocení modelů |
| Rodina | MCDM | MCDM |
| Rok vzniku≠ | 2005 | 1979 |
| Tvůrce≠ | Danon, Diaz-Guilera, Duch, Arenas | David L. Davies, Donald W. Bouldin |
| Typ≠ | Information-theoretic metric | Cluster quality metric |
| Původní zdroj≠ | Danon, L., Diaz-Guilera, A., Duch, J., & Arenas, A. (2005). Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment, 2005(09), P09008. DOI ↗ | Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2), 224-227. DOI ↗ |
| Další názvy≠ | NMI, mutual information, information criterion | DBI, Davies Bouldin index |
| Příbuzné | 5 | 5 |
| Shrnutí≠ | Normalized Mutual Information (NMI), popularized by Danon et al. in 2005, is an external clustering evaluation metric based on information theory. It measures the amount of information shared between a predicted clustering and ground truth labels, normalized to a scale between 0 and 1. A value of 1 indicates perfect agreement, while 0 indicates independence. | 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. |
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