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| Δείκτης Dunn× | Μέθοδος Αγκώνα× | |
|---|---|---|
| Πεδίο | Αξιολόγηση Μοντέλων | Αξιολόγηση Μοντέλων |
| Οικογένεια | MCDM | MCDM |
| Έτος προέλευσης≠ | 1974 | 1953 |
| Δημιουργός≠ | Joseph C. Dunn | Robert Thorndike |
| Τύπος≠ | Cluster quality metric | Heuristic optimization criterion |
| Θεμελιώδης πηγή≠ | Dunn, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4(1), 95-104. DOI ↗ | Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics. link ↗ |
| Εναλλακτικές ονομασίες | Dunn's index, separation coefficient | elbow analysis, knee detection |
| Συναφείς | 5 | 5 |
| Σύνοψη≠ | 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. |
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