dc.creator | Martínez Ballesteros, María del Mar | es |
dc.creator | Martínez Álvarez, Francisco | es |
dc.creator | Troncoso Lora, Alicia | es |
dc.creator | Riquelme Santos, José Cristóbal | es |
dc.date.accessioned | 2016-07-08T09:03:20Z | |
dc.date.available | 2016-07-08T09:03:20Z | |
dc.date.issued | 2011 | |
dc.identifier.citation | Martínez Ballesteros, M.d.M., Martínez Álvarez, F., Troncoso Lora, A. y Riquelme Santos, J.C. (2011). An evolutionary algorithm to discover quantitative association rules in multidimensional time series. Soft Computing, 15 (10), 2065-2084. | |
dc.identifier.issn | 1432-7643 | es |
dc.identifier.uri | http://hdl.handle.net/11441/43390 | |
dc.description.abstract | An evolutionary approach for finding existing
relationships among several variables of a multidimensional
time series is presented in this work. The proposed model to
discover these relationships is based on quantitative association
rules. This algorithm, called QARGA (Quantitative
Association Rules by Genetic Algorithm), uses a particular
codification of the individuals that allows solving two basic
problems. First, it does not perform a previous attribute
discretization and, second, it is not necessary to set which
variables belong to the antecedent or consequent. Therefore,
it may discover all underlying dependencies among
different variables. To evaluate the proposed algorithm
three experiments have been carried out. As initial step,
several public datasets have been analyzed with the purpose
of comparing with other existing evolutionary approaches.
Also, the algorithm has been applied to synthetic time series
(where the relationships are known) to analyze its potential
for discovering rules in time series. Finally, a real-world
multidimensional time series composed by several climatological
variables has been considered. All the results show
a remarkable performance of QARGA. | es |
dc.description.sponsorship | Ministerio de Ciencia y Tecnología TIN2007- 68084-C02-02 | es |
dc.description.sponsorship | Junta de Andalucia P07-TIC- 02611 | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | Soft Computing, 15 (10), 2065-2084. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Time series | es |
dc.subject | Quantitative association rules | es |
dc.subject | Evolutionary algorithms | es |
dc.subject | Data mining | es |
dc.title | An evolutionary algorithm to discover quantitative association rules in multidimensional time series | 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-C02-02 | es |
dc.relation.projectID | P07-TIC- 02611 | es |
dc.relation.publisherversion | http://dx.doi.org/10.1007/s00500-011-0705-4 | es |
dc.identifier.doi | 10.1007/s00500-011-0705-4 | es |
idus.format.extent | 20 p. | es |
dc.journaltitle | Soft Computing | es |
dc.publication.volumen | 15 | es |
dc.publication.issue | 10 | es |
dc.publication.initialPage | 2065 | es |
dc.publication.endPage | 2084 | es |
dc.identifier.idus | https://idus.us.es/xmlui/handle/11441/43390 | |
dc.contributor.funder | Ministerio de Ciencia y Tecnología (MCYT). España | |
dc.contributor.funder | Junta de Andalucía | |