Comparar métodos

	Método do Cotovelo	Coeficiente de Silhueta
Área	Avaliação de modelos	Avaliação de modelos
Família	MCDM	MCDM
Ano de origem	1953	1987
Autor original	Robert Thorndike	Peter Rousseeuw
Tipo	Heuristic optimization criterion	Cluster quality metric
Fonte seminal	Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics.	Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53-65.
Outros nomes	elbow analysis, knee detection	silhouette coefficient, silhouette index
Relacionados	5	5
Resumo	The Elbow Method is a heuristic for selecting the optimal number of clusters in partitional clustering. Introduced by Robert Thorndike in 1953, it involves fitting clustering models for increasing numbers of clusters and plotting the within-cluster sum of squares (WCSS) against the number of clusters. The 'elbow' occurs where the rate of WCSS decrease sharply changes, suggesting an optimal cluster count.	The Silhouette Coefficient, introduced by Peter Rousseeuw in 1987, is a metric that measures how similar an object is to its own cluster compared to other clusters. It ranges from -1 to 1, where values close to 1 indicate well-separated and cohesive clusters, values near 0 suggest overlapping clusters, and negative values indicate misclustered points.