Σύγκριση μεθόδων

	Μέθοδος Αγκώνα	Δείκτης Calinski-Harabasz	Δείκτης Davies-Bouldin
Πεδίο	Αξιολόγηση Μοντέλων	Αξιολόγηση Μοντέλων	Αξιολόγηση Μοντέλων
Οικογένεια	MCDM	MCDM	MCDM
Έτος προέλευσης	1953	1974	1979
Δημιουργός	Robert Thorndike	Tadeusz Calinski, Jerzy Harabasz	David L. Davies, Donald W. Bouldin
Τύπος	Heuristic optimization criterion	Cluster quality metric	Cluster quality metric
Θεμελιώδης πηγή	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.	Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2), 224-227.
Εναλλακτικές ονομασίες	elbow analysis, knee detection	variance ratio criterion, pseudo F-statistic, CH index	DBI, Davies Bouldin index
Συναφείς	5	5	5
Σύνοψη	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.	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.