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.date2019-08-08T15:16:10Z
dc.date2019-08-08T15:16:10Z
dc.date2019-04-27
dc.date.accessioned2023-10-03T19:12:37Z
dc.date.available2023-10-03T19:12:37Z
dc.identifier978-3-030-19222-8
dc.identifier978-3-030-19223-5
dc.identifierhttp://hdl.handle.net/11323/5136
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9168919
dc.descriptionThe 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.
dc.formatapplication/pdf
dc.languageeng
dc.publisherInternational Conference on Green, Pervasive, and Cloud Computing
dc.relationhttps://doi.org/10.1007/978-3-030-19223-5_3
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dc.rightsCC0 1.0 Universal
dc.rightshttp://creativecommons.org/publicdomain/zero/1.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subjectK-means
dc.subjectAutomatic clustering
dc.subjectDifferential evolution
dc.subjectK activation threshold
dc.subjectU control chart
dc.subjectAcademic efficiency (AE)
dc.titleU-Control Chart Based Differential Evolution Clustering for Determining the Number of Cluster in k-Means
dc.typePre-Publicación
dc.typehttp://purl.org/coar/resource_type/c_816b
dc.typeText
dc.typeinfo:eu-repo/semantics/preprint
dc.typeinfo:eu-repo/semantics/draft
dc.typehttp://purl.org/redcol/resource_type/ARTOTR
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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