Comparer des méthodes

	V-measure	Indice de Rand Ajusté
Domaine	Évaluation de modèles	Évaluation de modèles
Famille	MCDM	MCDM
Année d'origine	2007	1985
Auteur d'origine	Andrew Rosenberg, Julia Hirschberg	Lawrence Hubert, Phipps Arabie
Type	Entropy-based metric	External similarity 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).	Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2(1), 193-218.
Alias	V-measure score, homogeneity completeness V-measure	ARI, adjusted Rand coefficient
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 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.