विधियों की तुलना करें

	कैलिंस्की-हाराबास्ज़ सूचकांक	डन सूचकांक	गैप स्टैटिस्टिक (Gap Statistic)
क्षेत्र	मॉडल मूल्यांकन	मॉडल मूल्यांकन	मॉडल मूल्यांकन
परिवार	MCDM	MCDM	MCDM
उद्भव वर्ष	1974	1974	2001
प्रवर्तक	Tadeusz Calinski, Jerzy Harabasz	Joseph C. Dunn	Robert Tibshirani, Guenther Walther, Trevor Hastie
प्रकार	Cluster quality metric	Cluster quality metric	Statistical criterion
मौलिक स्रोत	Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3(1), 1-27.	Dunn, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4(1), 95-104.	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.
उपनाम	variance ratio criterion, pseudo F-statistic, CH index	Dunn's index, separation coefficient	gap index, Tibshirani gap statistic
संबंधित	5	5	5
सारांश	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 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 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.