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| Gap Statistic× | Indeks Calinski-Harabasz× | Indeks Davies-Bouldin× | |
|---|---|---|---|
| Bidang | Penilaian Model | Penilaian Model | Penilaian Model |
| Keluarga | MCDM | MCDM | MCDM |
| Tahun asal≠ | 2001 | 1974 | 1979 |
| Pengasas≠ | Robert Tibshirani, Guenther Walther, Trevor Hastie | Tadeusz Calinski, Jerzy Harabasz | David L. Davies, Donald W. Bouldin |
| Jenis≠ | Statistical criterion | Cluster quality metric | Cluster quality metric |
| Sumber perintis≠ | 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 ↗ | Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3(1), 1-27. DOI ↗ | Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2), 224-227. DOI ↗ |
| Alias≠ | gap index, Tibshirani gap statistic | variance ratio criterion, pseudo F-statistic, CH index | DBI, Davies Bouldin index |
| Berkaitan | 5 | 5 | 5 |
| Ringkasan≠ | 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. | The Calinski-Harabasz Index, also called the Variance Ratio Criterion, was introduced by Calinski and Harabasz in 1974. It is a metric that measures the ratio of between-cluster variance to within-cluster variance, adjusted for the number of clusters and data points. Higher values indicate better-separated, more compact clusters. | The Davies-Bouldin Index, introduced by Davies and Bouldin in 1979, is a metric for evaluating clustering quality based on the average similarity between each cluster and its most similar neighboring cluster. Lower values indicate better clustering, with a minimum of 0 representing perfectly separated, non-overlapping clusters. |
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