Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Metoda ohybu (Elbow Method)× | Setrvačnost× | |
|---|---|---|
| Obor | Hodnocení modelů | Hodnocení modelů |
| Rodina | MCDM | MCDM |
| Rok vzniku≠ | 1953 | 1967 |
| Tvůrce≠ | Robert Thorndike | Stuart Lloyd, James MacQueen |
| Typ≠ | Heuristic optimization criterion | Clustering quality metric |
| Původní zdroj≠ | Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics. link ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137. DOI ↗ |
| Další názvy≠ | elbow analysis, knee detection | WCSS, within-cluster sum of squares, cluster cohesion |
| Příbuzné | 5 | 5 |
| Shrnutí≠ | 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. |
| ScholarGateDatová sada ↗ |
|
|