方法对比

	V-measure	归一化互信息
领域	模型评估	模型评估
方法族	MCDM	MCDM
起源年份	2007	2005
提出者	Andrew Rosenberg, Julia Hirschberg	Danon, Diaz-Guilera, Duch, Arenas
类型	Entropy-based metric	Information-theoretic 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).	Danon, L., Diaz-Guilera, A., Duch, J., & Arenas, A. (2005). Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment, 2005(09), P09008.
别名	V-measure score, homogeneity completeness V-measure	NMI, mutual information, information criterion
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.	Normalized Mutual Information (NMI), popularized by Danon et al. in 2005, is an external clustering evaluation metric based on information theory. It measures the amount of information shared between a predicted clustering and ground truth labels, normalized to a scale between 0 and 1. A value of 1 indicates perfect agreement, while 0 indicates independence.