Linganisha mbinu

	Kielezo cha Calinski-Harabasz	Kielezo cha Davies-Bouldin	Dunn Index	Inertia
Nyanja	Tathmini ya Modeli	Tathmini ya Modeli	Tathmini ya Modeli	Tathmini ya Modeli
Familia	MCDM	MCDM	MCDM	MCDM
Mwaka wa asili	1974	1979	1974	1967
Mwanzilishi	Tadeusz Calinski, Jerzy Harabasz	David L. Davies, Donald W. Bouldin	Joseph C. Dunn	Stuart Lloyd, James MacQueen
Aina	Cluster quality metric	Cluster quality metric	Cluster quality metric	Clustering quality metric
Chanzo asilia	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.	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.
Majina mbadala	variance ratio criterion, pseudo F-statistic, CH index	DBI, Davies Bouldin index	Dunn's index, separation coefficient	WCSS, within-cluster sum of squares, cluster cohesion
Zinazohusiana	5	5	5	5
Muhtasari	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 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.