A visual analytics approach for exploration of high-dimensional time series based on Neighbor-Joining Tree

Roberto Rodríguez, Reynaldo Alfonte, Ana María Cuadros Valdivia

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

High-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 wellknown similarity measures and interaction mechanisms to do exploratory analysis of high-dimensional time series data interactively.

Idioma originalInglés
Título de la publicación alojadaProceedings of the 10th International Conference on Computer Modeling and Simulation, ICCMS 2018
EditorialAssociation for Computing Machinery
Páginas44-48
Número de páginas5
ISBN (versión digital)9781450363396
DOI
EstadoPublicada - 8 ene. 2018
Publicado de forma externa
Evento10th International Conference on Computer Modeling and Simulation, ICCMS 2018 - Sydney, Australia
Duración: 8 ene. 201810 ene. 2018

Serie de la publicación

NombreACM International Conference Proceeding Series

Conferencia

Conferencia10th International Conference on Computer Modeling and Simulation, ICCMS 2018
País/TerritorioAustralia
CiudadSydney
Período8/01/1810/01/18

Nota bibliográfica

Publisher Copyright:
© 2018 Association for Computing Machinery.

Huella

Profundice en los temas de investigación de 'A visual analytics approach for exploration of high-dimensional time series based on Neighbor-Joining Tree'. En conjunto forman una huella única.

Citar esto