Salīdzināt metodes

	V-measure	Fowlkesa-Mallows indeks
Nozare	Modeļu novērtēšana	Modeļu novērtēšana
Saime	MCDM	MCDM
Izcelsmes gads	2007	1983
Autors	Andrew Rosenberg, Julia Hirschberg	E. B. Fowlkes, C. L. Mallows
Tips	Entropy-based metric	Pair-counting metric
Pirmavots	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).	Fowlkes, E. B., & Mallows, C. L. (1983). A method for comparing two hierarchical clusterings. Journal of the American Statistical Association, 78(383), 553-569.
Citi nosaukumi	V-measure score, homogeneity completeness V-measure	Fowlkes Mallows, FM index
Saistītās	5	5
Kopsavilkums	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 Fowlkes-Mallows Index, introduced by Fowlkes and Mallows in 1983, is an external clustering evaluation metric based on the geometric mean of precision and recall. It measures agreement between two partitions by examining pairs of points and how they are grouped in both the predicted and ground truth clusterings. Values range from 0 to 1, with 1 indicating perfect agreement.