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dc.contributor.authorTimón, Isabel
dc.contributor.authorSoto, Jesús
dc.contributor.authorCecilia Canales, José María
dc.contributor.authorPérez Sánchez, Horacio
dc.date.accessioned2018-05-07T09:15:20Z
dc.date.available2018-05-07T09:15:20Z
dc.date.issued2016-04-15
dc.identifier.issn0957-4174
dc.identifier.urihttp://hdl.handle.net/10952/3043
dc.description.abstractClustering aims to classify different patterns into groups called clusters. Many algorithms for both hard and fuzzy clustering have been developed to deal with exploratory data analysis in many contexts such as image processing, pattern recognition, etc. However, we are witnessing the era of big data computing where computing resources are becoming the main bottleneck to deal with those large datasets. In this context, sequential algorithms need to be redesigned and even rethought to fully leverage the emergent massively parallel architectures. In this paper, we propose a parallel implementation of the fuzzy minimals clustering algorithm called Parallel Fuzzy Minimal (PFM). Our experimental results reveal linear speed-up of PFM when compared to the sequential counterpart version, keeping very good classification quality.es
dc.language.isoenes
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectParallel fuzzy clusteringes
dc.subjectFuzzy clusteringes
dc.subjectFuzzy minimalses
dc.titleParallel implementation of fuzzy minimals clustering algorithmes
dc.typearticlees
dc.rights.accessRightsopenAccesses
dc.journal.titleExpert Systems with Applicationses
dc.volume.number48es
dc.description.disciplineIngeniería, Industria y Construcciónes


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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