Comparar métodos

	Índice de Dunn	Índice Calinski-Harabasz	Índice Davies-Bouldin
Campo	Evaluación de modelos	Evaluación de modelos	Evaluación de modelos
Familia	MCDM	MCDM	MCDM
Año de origen	1974	1974	1979
Autor original	Joseph C. Dunn	Tadeusz Calinski, Jerzy Harabasz	David L. Davies, Donald W. Bouldin
Tipo	Cluster quality metric	Cluster quality metric	Cluster quality metric
Fuente seminal	Dunn, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4(1), 95-104.	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.
Alias	Dunn's index, separation coefficient	variance ratio criterion, pseudo F-statistic, CH index	DBI, Davies Bouldin index
Relacionados	5	5	5
Resumen	The Dunn Index, introduced by Joseph C. Dunn in 1974, is a metric that captures cluster quality by measuring the ratio of the minimum between-cluster distance to the maximum within-cluster diameter. Higher values indicate well-separated and compact clusters, with better clustering quality.	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.