Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| V-measure× | Indice de Fowlkes-Mallows× | |
|---|---|---|
| Domaine | Évaluation de modèles | Évaluation de modèles |
| Famille | MCDM | MCDM |
| Année d'origine≠ | 2007 | 1983 |
| Auteur d'origine≠ | Andrew Rosenberg, Julia Hirschberg | E. B. Fowlkes, C. L. Mallows |
| Type≠ | Entropy-based metric | Pair-counting metric |
| Source fondatrice≠ | 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 ↗ | 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 ↗ |
| Alias | V-measure score, homogeneity completeness V-measure | Fowlkes Mallows, FM index |
| Apparentées | 5 | 5 |
| Résumé≠ | 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. | 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. |
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