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Weighted Complete Graphs for Condensing Data
dc.contributor.author | Guzmán-Ponce, Angélica | |
dc.contributor.author | Marcial-Romero, J. Raymundo | |
dc.contributor.author | Valdovinos Rosas, Rosa María | |
dc.contributor.author | Sánchez Garreta, Josep Salvador | |
dc.date.accessioned | 2021-11-29T15:11:32Z | |
dc.date.available | 2021-11-29T15:11:32Z | |
dc.date.issued | 2020-12-01 | |
dc.identifier.citation | Guzmán-Ponce, A., Marcial-Romero, J. R., Valdovinos-Rosas, R. M., & Sánchez-Garreta, J. S. (2020). Weighted Complete Graphs for Condensing Data. Electronic Notes in Theoretical Computer Science, 354, 45-60. | ca_CA |
dc.identifier.issn | 1571-0661 | |
dc.identifier.uri | http://hdl.handle.net/10234/195693 | |
dc.description.abstract | In many real-world problems (such as industrial applications, chemistry models, social network analysis, among others), their solution can be obtained by transforming the problem in terms of vertices and edges, that is to say, using graph theory. Data Science applications are characterized by processing large volumes of data, in some cases, the data size can be higher than the resources for their processing, situation that makes prohibitive to use the traditional methods. In this way, to develop solutions based on graphs for condensing data can be a good strategy for handling big datasets. In this paper we include two methods for condensing data based on graphs, the two proposals consider a weighted complete graph by acquiring an induced subgraph or a minimum spanning tree from the whole datasets. We conducted some experiments in order to validate our proposals, using 24 benchmark real-datasets for training the 1NN, C4.5, and SVM classifiers. The results prove that our methods condensed the datasets without reducing the performance of the classifier, in terms of geometric means and the Wilcoxon’s test. | ca_CA |
dc.format.extent | 16 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Elsevier B.V. | ca_CA |
dc.relation | UAEM proyect | ca_CA |
dc.relation.isPartOf | Electronic Notes in Theoretical Computer Science, Vol. 354 (december 2020) | ca_CA |
dc.rights | © 2020 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). | ca_CA |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | ca_CA |
dc.subject | weighted graph | ca_CA |
dc.subject | induced subgraph | ca_CA |
dc.subject | minimum spanning tree | ca_CA |
dc.subject | condensed data | ca_CA |
dc.subject | data science | ca_CA |
dc.title | Weighted Complete Graphs for Condensing Data | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1016/j.entcs.2020.10.005 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
project.funder.name | Universitat Jaume I | ca_CA |
project.funder.name | Mexican CONACYT | ca_CA |
oaire.awardNumber | UJI-B2018-49 | ca_CA |
oaire.awardNumber | 5046/2020CIC | ca_CA |
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