Salīdzināt metodes

	Inerce	Dunn indekss	Elkoņa metode
Nozare	Modeļu novērtēšana	Modeļu novērtēšana	Modeļu novērtēšana
Saime	MCDM	MCDM	MCDM
Izcelsmes gads	1967	1974	1953
Autors	Stuart Lloyd, James MacQueen	Joseph C. Dunn	Robert Thorndike
Tips	Clustering quality metric	Cluster quality metric	Heuristic optimization criterion
Pirmavots	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137.	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.
Citi nosaukumi	WCSS, within-cluster sum of squares, cluster cohesion	Dunn's index, separation coefficient	elbow analysis, knee detection
Saistītās	5	5	5
Kopsavilkums	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.	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.