Compara mètodes

	Índex Calinski-Harabasz	Índex Davies-Bouldin	Estadística de la bretxa
Camp	Avaluació de models	Avaluació de models	Avaluació de models
Família	MCDM	MCDM	MCDM
Any d'origen	1974	1979	2001
Autor original	Tadeusz Calinski, Jerzy Harabasz	David L. Davies, Donald W. Bouldin	Robert Tibshirani, Guenther Walther, Trevor Hastie
Tipus	Cluster quality metric	Cluster quality metric	Statistical criterion
Font seminal	Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3(1), 1-27.	Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2), 224-227.	Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2), 411-423.
Àlies	variance ratio criterion, pseudo F-statistic, CH index	DBI, Davies Bouldin index	gap index, Tibshirani gap statistic
Relacionats	5	5	5
Resum	The Calinski-Harabasz Index, also called the Variance Ratio Criterion, was introduced by Calinski and Harabasz in 1974. It is a metric that measures the ratio of between-cluster variance to within-cluster variance, adjusted for the number of clusters and data points. Higher values indicate better-separated, more compact clusters.	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.	The Gap Statistic, developed by Tibshirani, Walther, and Hastie in 2001, is a principled statistical method for determining the optimal number of clusters in a dataset. It compares the observed within-cluster sum of squares to the expected value under a null hypothesis of no clustering structure, providing a theoretically grounded approach to cluster number selection.