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| 邓恩指数× | Calinski-Harabasz指数× | 惯性× | |
|---|---|---|---|
| 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 | 1974 | 1967 |
| 提出者≠ | Joseph C. Dunn | Tadeusz Calinski, Jerzy Harabasz | Stuart Lloyd, James MacQueen |
| 类型≠ | Cluster quality metric | Cluster quality metric | Clustering quality metric |
| 开创性文献≠ | Dunn, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4(1), 95-104. DOI ↗ | Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3(1), 1-27. DOI ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137. DOI ↗ |
| 别名≠ | Dunn's index, separation coefficient | variance ratio criterion, pseudo F-statistic, CH index | WCSS, within-cluster sum of squares, cluster cohesion |
| 相关 | 5 | 5 | 5 |
| 摘要≠ | 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 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. | 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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