Comparer des méthodes

	V-measure	Indice de Davies-Bouldin
Domaine	Évaluation de modèles	Évaluation de modèles
Famille	MCDM	MCDM
Année d'origine	2007	1979
Auteur d'origine	Andrew Rosenberg, Julia Hirschberg	David L. Davies, Donald W. Bouldin
Type	Entropy-based metric	Cluster quality metric
Source fondatrice	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).	Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2), 224-227.
Alias	V-measure score, homogeneity completeness V-measure	DBI, Davies Bouldin index
Apparentées	5	5
Résumé	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 Davies-Bouldin Index, introduced by Davies and Bouldin in 1979, is a metric for evaluating clustering quality based on the average similarity between each cluster and its most similar neighboring cluster. Lower values indicate better clustering, with a minimum of 0 representing perfectly separated, non-overlapping clusters.