Usporedite metode

	Indeks Calinski-Harabasz	Dunn-ov indeks	Metoda lakta
Područje	Evaluacija modela	Evaluacija modela	Evaluacija modela
Obitelj	MCDM	MCDM	MCDM
Godina nastanka	1974	1974	1953
Tvorac	Tadeusz Calinski, Jerzy Harabasz	Joseph C. Dunn	Robert Thorndike
Vrsta	Cluster quality metric	Cluster quality metric	Heuristic optimization criterion
Temeljni izvor	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.	Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics.
Drugi nazivi	variance ratio criterion, pseudo F-statistic, CH index	Dunn's index, separation coefficient	elbow analysis, knee detection
Srodne	5	5	5
Sažetak	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 Elbow Method is a heuristic for selecting the optimal number of clusters in partitional clustering. Introduced by Robert Thorndike in 1953, it involves fitting clustering models for increasing numbers of clusters and plotting the within-cluster sum of squares (WCSS) against the number of clusters. The 'elbow' occurs where the rate of WCSS decrease sharply changes, suggesting an optimal cluster count.