Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Reglas de Asociación Semi-supervisadas× | Propagación de Etiquetas× | |
|---|---|---|
| Campo | Aprendizaje automático | Aprendizaje automático |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2003–2010s | 2002 |
| Autor original≠ | Liu, B.; Hsu, W.; Ma, Y. (and subsequent researchers) | Zhu, X. & Ghahramani, Z. |
| Tipo≠ | Pattern mining with partial supervision | Graph-based semi-supervised classification |
| Fuente seminal≠ | Liu, B., Hsu, W., & Ma, Y. (2003). Integrating Classification and Association Rule Mining. In Proceedings of the 4th IEEE International Conference on Data Mining (ICDM), pp. 339–346. link ↗ | Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University. link ↗ |
| Alias | semi-supervised ARM, label-guided association rule mining, constrained association rule mining, semi-supervised pattern discovery | LP, label spreading, graph-based semi-supervised learning, harmonic label propagation |
| Relacionados≠ | 4 | 3 |
| Resumen≠ | Semi-supervised association rule mining extends classical association rule learning by incorporating a small amount of labeled data alongside a larger unlabeled dataset. It uses known class information or user-provided constraints to guide the discovery of rules that are both statistically frequent and semantically meaningful, bridging unsupervised pattern mining with light supervision. | Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data. |
| ScholarGateConjunto de datos ↗ |
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