Discovering quantitative association rules: A novel approach based on evolutionary algorithms
This work proposes a novel methodology to improve the discovery of quantitative association rules in continuous datasets. This methodology comprises several evolutionary algorithms able to deal with real-valued variables without performing a static discretization process. Additionally, several quali...
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2014 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/162014 |
| Acceso en línea: | https://hdl.handle.net/11441/162014 |
| Access Level: | acceso abierto |
| Palabra clave: | Data mining Evolutionary algorithms Quantitative association rules |
| Sumario: | This work proposes a novel methodology to improve the discovery of quantitative association rules in continuous datasets. This methodology comprises several evolutionary algorithms able to deal with real-valued variables without performing a static discretization process. Additionally, several quality measures are analysed to select the set of measures to be optimized with the aim of finding high-quality rules. |
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