Uporedite metode

	Metoda lakta	Indeks Čalinski-Harabaš	Inercija
Oblast	Evaluacija modela	Evaluacija modela	Evaluacija modela
Porodica	MCDM	MCDM	MCDM
Godina nastanka	1953	1974	1967
Tvorac	Robert Thorndike	Tadeusz Calinski, Jerzy Harabasz	Stuart Lloyd, James MacQueen
Tip	Heuristic optimization criterion	Cluster quality metric	Clustering quality metric
Temeljni izvor	Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics.	Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3(1), 1-27.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137.
Drugi nazivi	elbow analysis, knee detection	variance ratio criterion, pseudo F-statistic, CH index	WCSS, within-cluster sum of squares, cluster cohesion
Srodne	5	5	5
Sažetak	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.	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.	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.