Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| Normaliseret gensidig information× | V-measure× | |
|---|---|---|
| Fagområde | Modelevaluering | Modelevaluering |
| Familie | MCDM | MCDM |
| Oprindelsesår≠ | 2005 | 2007 |
| Ophavsperson≠ | Danon, Diaz-Guilera, Duch, Arenas | Andrew Rosenberg, Julia Hirschberg |
| Type≠ | Information-theoretic metric | Entropy-based metric |
| Oprindelig kilde≠ | 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 ↗ | Rosenberg, A., & Hirschberg, J. (2007). V-measure: A conditional entropy-based external cluster evaluation measure. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (pp. 410-420). link ↗ |
| Aliasser≠ | NMI, mutual information, information criterion | V-measure score, homogeneity completeness V-measure |
| Relaterede | 5 | 5 |
| Resumé≠ | 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. | V-measure, introduced by Rosenberg and Hirschberg in 2007, is an external clustering evaluation metric based on the harmonic mean of homogeneity and completeness. It measures whether clusters contain only points from a single true class (homogeneity) and whether all points from a true class are assigned to the same cluster (completeness). Values range from 0 to 1. |
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