Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Dunn indekss× | Inerce× | |
|---|---|---|
| Nozare | Modeļu novērtēšana | Modeļu novērtēšana |
| Saime | MCDM | MCDM |
| Izcelsmes gads≠ | 1974 | 1967 |
| Autors≠ | Joseph C. Dunn | Stuart Lloyd, James MacQueen |
| Tips≠ | Cluster quality metric | Clustering quality metric |
| Pirmavots≠ | Dunn, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4(1), 95-104. DOI ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137. DOI ↗ |
| Citi nosaukumi≠ | Dunn's index, separation coefficient | WCSS, within-cluster sum of squares, cluster cohesion |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. | 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. |
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