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| Calinski-Harabasz指数× | 戴维斯-布尔丁指数× | 邓恩指数× | |
|---|---|---|---|
| 领域 | 模型评估 | 模型评估 | 模型评估 |
| 方法族 | MCDM | MCDM | MCDM |
| 起源年份≠ | 1974 | 1979 | 1974 |
| 提出者≠ | Tadeusz Calinski, Jerzy Harabasz | David L. Davies, Donald W. Bouldin | Joseph C. Dunn |
| 类型 | Cluster quality metric | Cluster quality metric | Cluster quality metric |
| 开创性文献≠ | 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 ↗ | Dunn, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4(1), 95-104. DOI ↗ |
| 别名≠ | variance ratio criterion, pseudo F-statistic, CH index | DBI, Davies Bouldin index | Dunn's index, separation coefficient |
| 相关 | 5 | 5 | 5 |
| 摘要≠ | 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. | 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. |
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