dc.creator | Martínez Ballesteros, María del Mar | es |
dc.creator | Troncoso Lora, Alicia | es |
dc.creator | Martínez Álvarez, Francisco | es |
dc.creator | Riquelme Santos, José Cristóbal | es |
dc.date.accessioned | 2022-04-27T07:48:03Z | |
dc.date.available | 2022-04-27T07:48:03Z | |
dc.date.issued | 2010 | |
dc.identifier.citation | Martínez Ballesteros, M.d.M., Troncoso Lora, A., Martínez Álvarez, F. y Riquelme Santos, J.C. (2010). Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution. Integrated Computer-Aided Engineering, 17 (3), 227-242. | |
dc.identifier.issn | 1069-2509 | es |
dc.identifier.uri | https://hdl.handle.net/11441/132715 | |
dc.description.abstract | This research presents the mining of quantitative association rules based on evolutionary computation techniques.
First, a real-coded genetic algorithm that extends the well-known binary-coded CHC algorithm has been projected to determine
the intervals that define the rules without needing to discretize the attributes. The proposed algorithm is evaluated in synthetic
datasets under different levels of noise in order to test its performance and the reported results are then compared to that of
a multi-objective differential evolution algorithm, recently published. Furthermore, rules from real-world time series such as
temperature, humidity, wind speed and direction of the wind, ozone, nitrogen monoxide and sulfur dioxide have been discovered
with the objective of finding all existing relations between atmospheric pollution and climatological conditions. | es |
dc.description.sponsorship | Ministerio de Ciencia y Tecnología TIN2007-68084-C-00 | es |
dc.description.sponsorship | Junta de Andalucía P07-TIC-02611 | es |
dc.format | application/pdf | es |
dc.format.extent | 16 | es |
dc.language.iso | eng | es |
dc.publisher | IOS Press | es |
dc.relation.ispartof | Integrated Computer-Aided Engineering, 17 (3), 227-242. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Data mining | es |
dc.subject | Evolutionary algorithms | es |
dc.subject | Quantitative association rules | es |
dc.title | Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | TIN2007-68084-C-00 | es |
dc.relation.projectID | P07-TIC-02611 | es |
dc.relation.publisherversion | https://content.iospress.com/articles/integrated-computer-aided-engineering/ica00340 | es |
dc.identifier.doi | 10.3233/ICA-2010-0340 | es |
dc.contributor.group | Universidad de Sevilla. TIC-254: Data Science and Big Data Lab | es |
dc.journaltitle | Integrated Computer-Aided Engineering | es |
dc.publication.volumen | 17 | es |
dc.publication.issue | 3 | es |
dc.publication.initialPage | 227 | es |
dc.publication.endPage | 242 | es |
dc.identifier.sisius | 6626688 | es |
dc.contributor.funder | Ministerio de Ciencia Y Tecnología (MCYT). España | es |
dc.contributor.funder | Junta de Andalucía | es |