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Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.

Índice Calinski-Harabasz×Estatística Gap×Inércia×
ÁreaAvaliação de modelosAvaliação de modelosAvaliação de modelos
FamíliaMCDMMCDMMCDM
Ano de origem197420011967
Autor originalTadeusz Calinski, Jerzy HarabaszRobert Tibshirani, Guenther Walther, Trevor HastieStuart Lloyd, James MacQueen
TipoCluster quality metricStatistical criterionClustering quality metric
Fonte seminalCalinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3(1), 1-27. DOI ↗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. DOI ↗Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137. DOI ↗
Outros nomesvariance ratio criterion, pseudo F-statistic, CH indexgap index, Tibshirani gap statisticWCSS, within-cluster sum of squares, cluster cohesion
Relacionados555
ResumoThe 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 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.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.
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ScholarGateComparar métodos: Calinski-Harabasz Index · Gap Statistic · Inertia (Within-Cluster Sum of Squares). Recuperado em 2026-06-20 de https://scholargate.app/pt/compare