Sammenlign metoder

	Gap Statistic	Calinski-Harabasz-indeksen	Davies-Bouldin-indeksen	Treghet
Fagfelt	Modellevaluering	Modellevaluering	Modellevaluering	Modellevaluering
Familie	MCDM	MCDM	MCDM	MCDM
Opprinnelsesår	2001	1974	1979	1967
Opphavsperson	Robert Tibshirani, Guenther Walther, Trevor Hastie	Tadeusz Calinski, Jerzy Harabasz	David L. Davies, Donald W. Bouldin	Stuart Lloyd, James MacQueen
Type	Statistical criterion	Cluster quality metric	Cluster quality metric	Clustering quality metric
Opprinnelig kilde	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.	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.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137.
Alias	gap index, Tibshirani gap statistic	variance ratio criterion, pseudo F-statistic, CH index	DBI, Davies Bouldin index	WCSS, within-cluster sum of squares, cluster cohesion
Relaterte	5	5	5	5
Sammendrag	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 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.	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.