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dc.contributor.authorSoto, Jesús
dc.contributor.authorMorales Helguera, Aliuska
dc.contributor.authorPérez Garrido, Alfonso
dc.contributor.authorGirón Rodríguez, Francisco
dc.contributor.authorBueno Crespo, Andrés
dc.contributor.authorPérez Sánchez, Horacio
dc.date.accessioned2018-05-09T11:38:42Z
dc.date.available2018-05-09T11:38:42Z
dc.date.issued2017
dc.identifier.otherhttp://dx.doi.org/10.1016/j.chemolab.2017.04.006
dc.identifier.urihttp://hdl.handle.net/10952/3084
dc.description.abstractVarious methods are used to make the partition of data sets for QSAR development and model validation. In this work we used a fuzzy minimals partitioning and we compare this methodology with another rational partition methods like k-means clustering (KMS) and Minimal Test Set Dissimilarity (MTSD). For the development of QSAR models Ordinary Least Squares (OLS) and Extreme Learning Machine (ELM) methods were used. The generated QSAR equations were validated by the coefficient of determination of the internal leave one out (LOO) cross validation method Q2LOO and then the coefficient of the external test set Q2ext was compared between partition methods. The results of this comparison showed that using fuzzy minimal for big and structurally diverse data sets gave an applicability domain similar to KMS and a better predictability models than both methods, KMS and MTSD.es
dc.language.isoenes
dc.publisherElsevieres
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectData partitiones
dc.subjectFuzzy clusteringes
dc.subjectRegressiones
dc.subjectExtreme Learning Machinees
dc.subjectMinimal Test Set Dissimilarityes
dc.subjectQSARes
dc.subjectValidationes
dc.subjectKappa-means clusteringes
dc.titleFuzzy clustering as rational partition method for QSARes
dc.typearticlees
dc.rights.accessRightsopenAccesses
dc.journal.titleChemometrics and Intelligent Laboratory Systemses
dc.volume.number166es
dc.description.disciplineCiencias Ambientaleses
dc.description.disciplineCiencias de la Alimentaciónes
dc.description.disciplineFarmaciaes
dc.description.disciplineIngeniería, Industria y Construcciónes
dc.description.disciplineMedicinaes


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional