Σύγκριση μεθόδων

	Δείκτης Dunn	Δείκτης Calinski-Harabasz	Δείκτης Davies-Bouldin	Στατιστική Κενού
Πεδίο	Αξιολόγηση Μοντέλων	Αξιολόγηση Μοντέλων	Αξιολόγηση Μοντέλων	Αξιολόγηση Μοντέλων
Οικογένεια	MCDM	MCDM	MCDM	MCDM
Έτος προέλευσης	1974	1974	1979	2001
Δημιουργός	Joseph C. Dunn	Tadeusz Calinski, Jerzy Harabasz	David L. Davies, Donald W. Bouldin	Robert Tibshirani, Guenther Walther, Trevor Hastie
Τύπος	Cluster quality metric	Cluster quality metric	Cluster quality metric	Statistical criterion
Θεμελιώδης πηγή	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.	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.
Εναλλακτικές ονομασίες	Dunn's index, separation coefficient	variance ratio criterion, pseudo F-statistic, CH index	DBI, Davies Bouldin index	gap index, Tibshirani gap statistic
Συναφείς	5	5	5	5
Σύνοψη	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.	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.