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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.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
dc.publisherInternational Conference on Green, Pervasive, and Cloud Computingspa
dc.rightsCC0 1.0 Universal*
dc.subjectAutomatic clusteringspa
dc.subjectDifferential evolutionspa
dc.subjectK activation thresholdspa
dc.subjectU control chartspa
dc.subjectAcademic efficiency (AE)spa
dc.titleU-Control Chart Based Differential Evolution Clustering for Determining the Number of Cluster in k-Meansspa
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