Compară metode

	Statistica Gap (Gap Statistic)	Indexul Davies-Bouldin	Metoda cotului
Domeniu	Evaluarea modelelor	Evaluarea modelelor	Evaluarea modelelor
Familie	MCDM	MCDM	MCDM
Anul apariției	2001	1979	1953
Autorul original	Robert Tibshirani, Guenther Walther, Trevor Hastie	David L. Davies, Donald W. Bouldin	Robert Thorndike
Tip	Statistical criterion	Cluster quality metric	Heuristic optimization criterion
Sursa seminală	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.	Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2), 224-227.	Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics.
Denumiri alternative	gap index, Tibshirani gap statistic	DBI, Davies Bouldin index	elbow analysis, knee detection
Înrudite	5	5	5
Rezumat	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.	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 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.