A visual analytics approach for exploration of high-dimensional time series based on neighbor-joining tree

Roberto Rodriguez Urquiaga, Ana Maria Cuadros Valdivia, Reynaldo Alfonte Zapana

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

Idioma originalInglés
Título de la publicación alojada2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas325-330
Número de páginas6
ISBN (versión digital)9781538646625
DOI
EstadoPublicada - 18 jun. 2018
Publicado de forma externa
Evento17th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017 - Bilbao, Espana
Duración: 18 dic. 201720 dic. 2017

Serie de la publicación

Nombre2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017

Conferencia

Conferencia17th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017
País/TerritorioEspana
CiudadBilbao
Período18/12/1720/12/17

Nota bibliográfica

Publisher Copyright:
© 2017 IEEE.

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