Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Dunn-indeksen× | Gap Statistic× | |
|---|---|---|
| Fagfelt | Modellevaluering | Modellevaluering |
| Familie | MCDM | MCDM |
| Opprinnelsesår≠ | 1974 | 2001 |
| Opphavsperson≠ | Joseph C. Dunn | Robert Tibshirani, Guenther Walther, Trevor Hastie |
| Type≠ | Cluster quality metric | Statistical criterion |
| Opprinnelig kilde≠ | Dunn, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4(1), 95-104. DOI ↗ | Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2), 411-423. DOI ↗ |
| Alias | Dunn's index, separation coefficient | gap index, Tibshirani gap statistic |
| Relaterte | 5 | 5 |
| Sammendrag≠ | 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 Gap Statistic, developed by Tibshirani, Walther, and Hastie in 2001, is a principled statistical method for determining the optimal number of clusters in a dataset. It compares the observed within-cluster sum of squares to the expected value under a null hypothesis of no clustering structure, providing a theoretically grounded approach to cluster number selection. |
| ScholarGateDatasett ↗ |
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