Linganisha mbinu

	Inertia	Kielezo cha Davies-Bouldin	Dunn Index	Njia ya Kiwiko
Nyanja	Tathmini ya Modeli	Tathmini ya Modeli	Tathmini ya Modeli	Tathmini ya Modeli
Familia	MCDM	MCDM	MCDM	MCDM
Mwaka wa asili	1967	1979	1974	1953
Mwanzilishi	Stuart Lloyd, James MacQueen	David L. Davies, Donald W. Bouldin	Joseph C. Dunn	Robert Thorndike
Aina	Clustering quality metric	Cluster quality metric	Cluster quality metric	Heuristic optimization criterion
Chanzo asilia	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137.	Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2), 224-227.	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.
Majina mbadala	WCSS, within-cluster sum of squares, cluster cohesion	DBI, Davies Bouldin index	Dunn's index, separation coefficient	elbow analysis, knee detection
Zinazohusiana	5	5	5	5
Muhtasari	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 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.	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.