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dc.contributor.authorAtencia-Ruiz, Miguel Alejandro 
dc.contributor.authorStoean, Catalin
dc.contributor.authorStoean, Ruxandra
dc.contributor.authorRodriguez Labrada, Roberto
dc.contributor.authorJoya-Caparrós, Gonzalo 
dc.date.accessioned2019-06-05T09:51:07Z
dc.date.available2019-06-05T09:51:07Z
dc.date.created2019
dc.date.issued2019-06-05
dc.identifier.urihttps://hdl.handle.net/10630/17771
dc.description.abstractIn this paper we introduce a novel methodology for unsupervised analysis of time series, based upon the iterative implementation of a clustering algorithm embedded into the evolution of a recurrent Echo State Network. The main features of the temporal data are captured by the dynamical evolution of the network states, which are then subject to a clustering procedure. We apply the proposed algorithm to time series coming from records of eye movements, called saccades, which are recorded for diagnosis of a neurodegenerative form of ataxia. This is a hard classification problem, since saccades from patients at an early stage of the disease are practically indistinguishable from those coming from healthy subjects. The unsupervised clustering algorithm implanted within the recurrent network produces more compact clusters, compared to conventional clustering of static data, and provides a source of information that could aid diagnosis and assessment of the disease.en_US
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Techen_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectRedes neuronales artificialesen_US
dc.subjectCongresos y conferenciasen_US
dc.subject.otherClusteringen_US
dc.subject.otherEcho State Networksen_US
dc.subject.otherk-meansen_US
dc.subject.otherTime seriesen_US
dc.subject.otherSaccadic eye movementen_US
dc.titleDynamic clustering of time series with Echo State Networksen_US
dc.typeinfo:eu-repo/semantics/workingPaperen_US
dc.centroEscuela de Ingenierías Industrialesen_US
dc.relation.eventtitle15th International Work-Conference on Artificial Neural Networksen_US
dc.relation.eventplaceGran Canariaen_US
dc.relation.eventdateJunio 2019en_US


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