השוואת שיטות

	שיטת המרפק	אינרציה
תחום	הערכת מודלים	הערכת מודלים
משפחה	MCDM	MCDM
שנת המקור	1953	1967
הוגה השיטה	Robert Thorndike	Stuart Lloyd, James MacQueen
סוג	Heuristic optimization criterion	Clustering quality metric
מקור מכונן	Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137.
כינויים	elbow analysis, knee detection	WCSS, within-cluster sum of squares, cluster cohesion
קשורות	5	5
תקציר	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.	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.