Compara mètodes

	V-measure	Índex Rand Ajustat
Camp	Avaluació de models	Avaluació de models
Família	MCDM	MCDM
Any d'origen	2007	1985
Autor original	Andrew Rosenberg, Julia Hirschberg	Lawrence Hubert, Phipps Arabie
Tipus	Entropy-based metric	External similarity metric
Font 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).	Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2(1), 193-218.
Àlies	V-measure score, homogeneity completeness V-measure	ARI, adjusted Rand coefficient
Relacionats	5	5
Resum	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.