A Method Non-Deterministic and Computationally Viable for Detecting Outliers in Large Datasets

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/106950
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dc.contributorGrupoM. Redes y Middlewarees_ES
dc.contributor.authorFernández Oliva, Alberto-
dc.contributor.authorMaciá Pérez, Francisco-
dc.contributor.authorBerna-Martinez, Jose Vicente-
dc.contributor.authorAbreu Ortega, Miguel-
dc.contributor.otherUniversidad de Alicante. Departamento de Tecnología Informática y Computaciónes_ES
dc.identifier.citationJournal of Information Science and Engineering. 2020, 36(3): 671-685. doi:10.6688/JISE.202005_36(3).0012es_ES
dc.description.abstractThis paper presents an outlier detection method that is based on a Variable Precision Rough Set Model (VPRSM). This method generalizes the standard set inclusion relation, which is the foundation of the Rough Sets Basic Model (RSBM). The main contribution of this research is an improvement in the quality of detection because this generalization allows us to classify when there is some degree of uncertainty. From the proposed method, a computationally viable algorithm for large volumes of data is also introduced. The experiments performed in a real scenario and a comparison of the results with the RSBM-based method demonstrate the efficiency of both the method and the algorithm in diverse contexts that involve large volumes of data.es_ES
dc.description.sponsorshipThis work has been supported by grant TIN2016-78103-C2-2-R, and University of Alicante projects GRE14-02 and Smart University.es_ES
dc.publisherInstitute of Information Science, Academia Sinicaes_ES
dc.rights© Institute of Information Science, Academia Sinicaes_ES
dc.subjectRough sets (RS)es_ES
dc.subjectRS basic model (RSBM)es_ES
dc.subjectVariable precision rough set model (VPRSM)es_ES
dc.subjectData setes_ES
dc.subjectData mininges_ES
dc.subject.otherArquitectura y Tecnología de Computadoreses_ES
dc.titleA Method Non-Deterministic and Computationally Viable for Detecting Outliers in Large Datasetses_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2016-78103-C2-2-R-
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