Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Index Fowlkes-Mallows× | Normalizovaná vzájemná informace× | |
|---|---|---|
| Obor | Hodnocení modelů | Hodnocení modelů |
| Rodina | MCDM | MCDM |
| Rok vzniku≠ | 1983 | 2005 |
| Tvůrce≠ | E. B. Fowlkes, C. L. Mallows | Danon, Diaz-Guilera, Duch, Arenas |
| Typ≠ | Pair-counting metric | Information-theoretic metric |
| Původní zdroj≠ | Fowlkes, E. B., & Mallows, C. L. (1983). A method for comparing two hierarchical clusterings. Journal of the American Statistical Association, 78(383), 553-569. DOI ↗ | 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 ↗ |
| Další názvy≠ | Fowlkes Mallows, FM index | NMI, mutual information, information criterion |
| Příbuzné | 5 | 5 |
| Shrnutí≠ | The Fowlkes-Mallows Index, introduced by Fowlkes and Mallows in 1983, is an external clustering evaluation metric based on the geometric mean of precision and recall. It measures agreement between two partitions by examining pairs of points and how they are grouped in both the predicted and ground truth clusterings. Values range from 0 to 1, with 1 indicating perfect agreement. | 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. |
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