Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Koonduse meetod× | Silueti koefitsient× | |
|---|---|---|
| Valdkond | Mudelite hindamine | Mudelite hindamine |
| Perekond | MCDM | MCDM |
| Tekkeaasta≠ | 1953 | 1987 |
| Looja≠ | Robert Thorndike | Peter Rousseeuw |
| Tüüp≠ | Heuristic optimization criterion | Cluster quality metric |
| Algallikas≠ | Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics. link ↗ | Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53-65. DOI ↗ |
| Rööpnimetused | elbow analysis, knee detection | silhouette coefficient, silhouette index |
| Seotud | 5 | 5 |
| Kokkuvõte≠ | 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. | The Silhouette Coefficient, introduced by Peter Rousseeuw in 1987, is a metric that measures how similar an object is to its own cluster compared to other clusters. It ranges from -1 to 1, where values close to 1 indicate well-separated and cohesive clusters, values near 0 suggest overlapping clusters, and negative values indicate misclustered points. |
| ScholarGateAndmestik ↗ |
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