方法对比
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| 福尔克斯-马洛斯指数× | 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≠ | 1983 | 2005 |
| 提出者≠ | E. B. Fowlkes, C. L. Mallows | Danon, Diaz-Guilera, Duch, Arenas |
| 类型≠ | Pair-counting metric | Information-theoretic metric |
| 开创性文献≠ | Fowlkes, E. B., & Mallows, C. L. (1983). A method for comparing two hierarchical clusterings. Journal of the American Statistical Association, 78(383), 553-569. 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≠ | Fowlkes Mallows, FM index | NMI, mutual information, information criterion |
| 相关 | 5 | 5 |
| 摘要≠ | The Fowlkes-Mallows Index, introduced by Fowlkes and Mallows in 1983, is an external clustering evaluation metric based on the geometric mean of precision and recall. It measures agreement between two partitions by examining pairs of points and how they are grouped in both the predicted and ground truth clusterings. Values range from 0 to 1, with 1 indicating perfect agreement. | 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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