Salīdzināt metodes

	Dunn indekss	Calinska-Harabaša indekss	Deivisa-Boldina indekss	Inerce
Nozare	Modeļu novērtēšana	Modeļu novērtēšana	Modeļu novērtēšana	Modeļu novērtēšana
Saime	MCDM	MCDM	MCDM	MCDM
Izcelsmes gads	1974	1974	1979	1967
Autors	Joseph C. Dunn	Tadeusz Calinski, Jerzy Harabasz	David L. Davies, Donald W. Bouldin	Stuart Lloyd, James MacQueen
Tips	Cluster quality metric	Cluster quality metric	Cluster quality metric	Clustering quality metric
Pirmavots	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.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137.
Citi nosaukumi	Dunn's index, separation coefficient	variance ratio criterion, pseudo F-statistic, CH index	DBI, Davies Bouldin index	WCSS, within-cluster sum of squares, cluster cohesion
Saistītās	5	5	5	5
Kopsavilkums	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.	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.