Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| V-measure× | Informação Mútua Normalizada× | |
|---|---|---|
| Área | Avaliação de modelos | Avaliação de modelos |
| Família | MCDM | MCDM |
| Ano de origem≠ | 2007 | 2005 |
| Autor original≠ | Andrew Rosenberg, Julia Hirschberg | Danon, Diaz-Guilera, Duch, Arenas |
| Tipo≠ | Entropy-based metric | Information-theoretic metric |
| Fonte seminal≠ | Rosenberg, A., & Hirschberg, J. (2007). V-measure: A conditional entropy-based external cluster evaluation measure. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (pp. 410-420). link ↗ | 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 ↗ |
| Outros nomes≠ | V-measure score, homogeneity completeness V-measure | NMI, mutual information, information criterion |
| Relacionados | 5 | 5 |
| Resumo≠ | V-measure, introduced by Rosenberg and Hirschberg in 2007, is an external clustering evaluation metric based on the harmonic mean of homogeneity and completeness. It measures whether clusters contain only points from a single true class (homogeneity) and whether all points from a true class are assigned to the same cluster (completeness). Values range from 0 to 1. | 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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