Sammenlign metoder

	Dunn-indeksen	Calinski-Harabasz-indeksen	Gap Statistic
Fagfelt	Modellevaluering	Modellevaluering	Modellevaluering
Familie	MCDM	MCDM	MCDM
Opprinnelsesår	1974	1974	2001
Opphavsperson	Joseph C. Dunn	Tadeusz Calinski, Jerzy Harabasz	Robert Tibshirani, Guenther Walther, Trevor Hastie
Type	Cluster quality metric	Cluster quality metric	Statistical criterion
Opprinnelig kilde	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.	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.
Alias	Dunn's index, separation coefficient	variance ratio criterion, pseudo F-statistic, CH index	gap index, Tibshirani gap statistic
Relaterte	5	5	5
Sammendrag	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 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.