So sánh phương pháp

	V-measure	Thông tin tương hỗ chuẩn hóa
Lĩnh vực	Đánh giá mô hình	Đánh giá mô hình
Họ	MCDM	MCDM
Năm ra đời	2007	2005
Người khởi xướng	Andrew Rosenberg, Julia Hirschberg	Danon, Diaz-Guilera, Duch, Arenas
Loại	Entropy-based metric	Information-theoretic metric
Công trình gốc	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.
Tên gọi khác	V-measure score, homogeneity completeness V-measure	NMI, mutual information, information criterion
Liên quan	5	5
Tóm tắt	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.