方法对比

	V-measure	调整兰德指数
领域	模型评估	模型评估
方法族	MCDM	MCDM
起源年份	2007	1985
提出者	Andrew Rosenberg, Julia Hirschberg	Lawrence Hubert, Phipps Arabie
类型	Entropy-based metric	External similarity metric
开创性文献	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.
别名	V-measure score, homogeneity completeness V-measure	ARI, adjusted Rand coefficient
相关	5	5
摘要	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.