手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| 正規化相互情報量（Normalized Mutual Information, NMI）× | 調整ランド指数（ARI）× | |
|---|---|---|
| 分野 | モデル評価 | モデル評価 |
| 系統 | MCDM | MCDM |
| 提唱年≠ | 2005 | 1985 |
| 提唱者≠ | Danon, Diaz-Guilera, Duch, Arenas | Lawrence Hubert, Phipps Arabie |
| 種類≠ | Information-theoretic metric | External similarity metric |
| 原典≠ | 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 ↗ | Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2(1), 193-218. DOI ↗ |
| 別名≠ | NMI, mutual information, information criterion | ARI, adjusted Rand coefficient |
| 関連 | 5 | 5 |
| 概要≠ | 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. | 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. |
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