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Semi-supervised incremental learning with few examples for discovering medical association rules
BACKGROUND: Association Rules are one of the main ways to represent structural patterns underlying raw data. They represent dependencies between sets of observations contained in the data. The associations established by these rules are very useful in the medical domain, for example in the predictiv...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8785547/ https://www.ncbi.nlm.nih.gov/pubmed/35073885 http://dx.doi.org/10.1186/s12911-022-01755-3 |