Porovnat metody

	Adjusted Rand Index	V-measure
Obor	Hodnocení modelů	Hodnocení modelů
Rodina	MCDM	MCDM
Rok vzniku	1985	2007
Tvůrce	Lawrence Hubert, Phipps Arabie	Andrew Rosenberg, Julia Hirschberg
Typ	External similarity metric	Entropy-based metric
Původní zdroj	Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2(1), 193-218.	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).
Další názvy	ARI, adjusted Rand coefficient	V-measure score, homogeneity completeness V-measure
Příbuzné	5	5
Shrnutí	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.	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.