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dc.creatorSilva, Jesús
dc.creatorPineda Lezama, Omar Bonerge
dc.creatorVarela, Noel
dc.creatorGarcía Guiliany, Jesús
dc.creatorSteffens Sanabria, Ernesto
dc.creatorSánchez Otero, Madelin
dc.creatorÁlvarez Rojas, Vladimir
dc.date.accessioned2019-08-08T15:16:10Z
dc.date.available2019-08-08T15:16:10Z
dc.date.issued2019-04-27
dc.identifier.isbn978-3-030-19222-8
dc.identifier.isbn978-3-030-19223-5
dc.identifier.urihttp://hdl.handle.net/11323/5136
dc.description.abstractThe automatic clustering differential evolution (ACDE) is one of the clustering methods that are able to determine the cluster number automatically. However, ACDE still makes use of the manual strategy to determine k activation threshold thereby affecting its performance. In this study, the ACDE problem will be ameliorated using the u-control chart (UCC) then the cluster number generated from ACDE will be fed to k-means. The performance of the proposed method was tested using six public datasets from the UCI repository about academic efficiency (AE) and evaluated with Davies Bouldin Index (DBI) and Cosine Similarity (CS) measure. The results show that the proposed method yields excellent performance compared to prior researches.es_ES
dc.language.isoenges_ES
dc.publisherInternational Conference on Green, Pervasive, and Cloud Computinges_ES
dc.relation.ispartofhttps://doi.org/10.1007/978-3-030-19223-5_3es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectK-meanses_ES
dc.subjectAutomatic clusteringes_ES
dc.subjectDifferential evolutiones_ES
dc.subjectK activation thresholdes_ES
dc.subjectU control chartes_ES
dc.subjectAcademic efficiency (AE)es_ES
dc.titleU-Control Chart Based Differential Evolution Clustering for Determining the Number of Cluster in k-Meanses_ES
dc.typePreprintes_ES
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