方法对比
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| 调整兰德指数× | 归一化互信息× | |
|---|---|---|
| 领域 | 模型评估 | 模型评估 |
| 方法族 | MCDM | MCDM |
| 起源年份≠ | 1985 | 2005 |
| 提出者≠ | Lawrence Hubert, Phipps Arabie | Danon, Diaz-Guilera, Duch, Arenas |
| 类型≠ | External similarity metric | Information-theoretic metric |
| 开创性文献≠ | Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2(1), 193-218. DOI ↗ | Danon, L., Diaz-Guilera, A., Duch, J., & Arenas, A. (2005). Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment, 2005(09), P09008. DOI ↗ |
| 别名≠ | ARI, adjusted Rand coefficient | NMI, mutual information, information criterion |
| 相关 | 5 | 5 |
| 摘要≠ | The Adjusted Rand Index (ARI), developed by Hubert and Arabie in 1985, is an external clustering evaluation metric that measures the agreement between a predicted clustering and a ground truth labeling. It ranges from -1 to 1, where 1 indicates perfect agreement, 0 indicates random clustering, and negative values indicate performance worse than random chance. | Normalized Mutual Information (NMI), popularized by Danon et al. in 2005, is an external clustering evaluation metric based on information theory. It measures the amount of information shared between a predicted clustering and ground truth labels, normalized to a scale between 0 and 1. A value of 1 indicates perfect agreement, while 0 indicates independence. |
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