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 original | Inglés |
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Título de la publicación alojada | Proceedings of the 10th International Conference on Computer Modeling and Simulation, ICCMS 2018 |
Editorial | Association for Computing Machinery |
Páginas | 44-48 |
Número de páginas | 5 |
ISBN (versión digital) | 9781450363396 |
DOI | |
Estado | Publicada - 8 ene. 2018 |
Publicado de forma externa | Sí |
Evento | 10th International Conference on Computer Modeling and Simulation, ICCMS 2018 - Sydney, Australia Duración: 8 ene. 2018 → 10 ene. 2018 |
Serie de la publicación
Nombre | ACM International Conference Proceeding Series |
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Conferencia
Conferencia | 10th International Conference on Computer Modeling and Simulation, ICCMS 2018 |
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País/Territorio | Australia |
Ciudad | Sydney |
Período | 8/01/18 → 10/01/18 |
Nota bibliográfica
Publisher Copyright:© 2018 Association for Computing Machinery.