विधियों की तुलना करें

	कैलिंस्की-हाराबास्ज़ सूचकांक	डन सूचकांक	जड़ता (Inertia)
क्षेत्र	मॉडल मूल्यांकन	मॉडल मूल्यांकन	मॉडल मूल्यांकन
परिवार	MCDM	MCDM	MCDM
उद्भव वर्ष	1974	1974	1967
प्रवर्तक	Tadeusz Calinski, Jerzy Harabasz	Joseph C. Dunn	Stuart Lloyd, James MacQueen
प्रकार	Cluster quality metric	Cluster quality metric	Clustering quality metric
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.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137.
उपनाम	variance ratio criterion, pseudo F-statistic, CH index	Dunn's index, separation coefficient	WCSS, within-cluster sum of squares, cluster cohesion
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.	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.