Сравнение методов

	Индекс Данна	Индекс Калински-Харабаса	Индекс Дэвиса-Болдина
Область	Оценка моделей	Оценка моделей	Оценка моделей
Семейство	MCDM	MCDM	MCDM
Год появления	1974	1974	1979
Автор метода	Joseph C. Dunn	Tadeusz Calinski, Jerzy Harabasz	David L. Davies, Donald W. Bouldin
Тип	Cluster quality metric	Cluster quality metric	Cluster quality metric
Основополагающий источник	Dunn, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4(1), 95-104.	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.
Другие названия	Dunn's index, separation coefficient	variance ratio criterion, pseudo F-statistic, CH index	DBI, Davies Bouldin index
Связанные	5	5	5
Сводка	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 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.