Vertaile menetelmiä

	Calinski-Harabasz-indeksi	Dunn-indeksi	Kyynärpäämenetelmä
Tieteenala	Mallien arviointi	Mallien arviointi	Mallien arviointi
Menetelmäperhe	MCDM	MCDM	MCDM
Syntyvuosi	1974	1974	1953
Kehittäjä	Tadeusz Calinski, Jerzy Harabasz	Joseph C. Dunn	Robert Thorndike
Tyyppi	Cluster quality metric	Cluster quality metric	Heuristic optimization criterion
Alkuperäislähde	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.
Rinnakkaisnimet	variance ratio criterion, pseudo F-statistic, CH index	Dunn's index, separation coefficient	elbow analysis, knee detection
Liittyvät	5	5	5
Tiivistelmä	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.