Variable-Length Differential Evolution for Numerical and Discrete Association Rule Mining
This paper proposes a variable-length Differential Evolution for Association Rule Mining. The proposed algorithm includes a novel representation of individuals, which can encode both numerical and discrete attributes in their original or absolute complement of the original intervals. The fitness fun...
Gardado en:
| Principais autores: | , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Assuntos: | |
| Acceso en liña: | https://ieeexplore.ieee.org/document/10376180/ |
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