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dc.contributor.authorRodriguez Urquiaga, Roberto
dc.contributor.authorCuadros Valdivia, Ana María
dc.contributor.authorAlfonte Zapana, Reynaldo
dc.date.accessioned2018-11-21T16:42:45Z
dc.date.available2018-11-21T16:42:45Z
dc.date.issued2018-06-21
dc.identifier.isbn978-1-5386-4662-5
dc.identifier.urihttp://repositorio.ulasalle.edu.pe/handle/20.500.12953/25
dc.description.abstractHigh-dimensional time series analysis through visual techniques poses many challenges due to the visualization solutions proposed until now for exploratory tasks are not well-oriented to high volume of data. When the data sets grow large, the visual alternatives do not allow for a good association between similar time series. With the aim to increase more alternatives, we introduce a visual analytic approach based on Neighbor-Joining similarity tree. The proposed approach internally consists of five time series dimension reduction techniques widely used, two well-known similarity measures and interaction mechanisms to do exploratory analysis of high-dimensional time series data interactively.es_ES
dc.description.uriTrabajo de investigaciónes_ES
dc.formatapplication/mswordeng_US
dc.language.isoengeng_US
dc.publisherUniversidad La Sallees_ES
dc.relationinfo:eu-repo/semantics/articlees_ES
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_ES
dc.sourceUniversidad La Sallees_ES
dc.subjectResearch Subject Categories::TECHNOLOGYes_ES
dc.titleA visual analytics approach for exploration of high-dimensional time series based on neighbor-joining treees_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.identifier.journal2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)es_ES
dc.description.peer-reviewDoble ciegoes_ES
dc.identifier.doi10.1109/ISSPIT.2017.8388663es_ES
dc.subject.ocdeResearch Subject Categories::TECHNOLOGYes_ES


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