Comparar métodos

	Índice de Dunn	Índice Calinski-Harabasz	Inercia
Campo	Evaluación de modelos	Evaluación de modelos	Evaluación de modelos
Familia	MCDM	MCDM	MCDM
Año de origen	1974	1974	1967
Autor original	Joseph C. Dunn	Tadeusz Calinski, Jerzy Harabasz	Stuart Lloyd, James MacQueen
Tipo	Cluster quality metric	Cluster quality metric	Clustering 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.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137.
Alias	Dunn's index, separation coefficient	variance ratio criterion, pseudo F-statistic, CH index	WCSS, within-cluster sum of squares, cluster cohesion
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.	Inertia, also called Within-Cluster Sum of Squares (WCSS), is a measure of cluster cohesion that quantifies how tightly points are grouped around their cluster centroids. Lower values indicate more compact, cohesive clusters. Inertia is the primary objective function for k-means clustering and has been a fundamental metric since the method's introduction.