Linganisha mbinu

	V-measure	Kielelezo cha Rand Kilichorekebishwa
Nyanja	Tathmini ya Modeli	Tathmini ya Modeli
Familia	MCDM	MCDM
Mwaka wa asili	2007	1985
Mwanzilishi	Andrew Rosenberg, Julia Hirschberg	Lawrence Hubert, Phipps Arabie
Aina	Entropy-based metric	External similarity metric
Chanzo asilia	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.
Majina mbadala	V-measure score, homogeneity completeness V-measure	ARI, adjusted Rand coefficient
Zinazohusiana	5	5
Muhtasari	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.