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.
|Title of host publication||Proceedings of the 10th International Conference on Computer Modeling and Simulation, ICCMS 2018|
|Publisher||Association for Computing Machinery|
|Number of pages||5|
|State||Published - 8 Jan 2018|
|Event||10th International Conference on Computer Modeling and Simulation, ICCMS 2018 - Sydney, Australia|
Duration: 8 Jan 2018 → 10 Jan 2018
|Name||ACM International Conference Proceeding Series|
|Conference||10th International Conference on Computer Modeling and Simulation, ICCMS 2018|
|Period||8/01/18 → 10/01/18|
Bibliographical noteFunding Information:
The authors would like to thank CONCYTEC (Consejo Nacional de Ciencia, Tecnología e Innovacíón Tecnológica), FONDECYT (Fondo Nacional de Desarrollo Científico y Tecnológico) and UNSA (Universidad Nacional SanAgustín) of Perú.
© 2018 Association for Computing Machinery.
- High dimensional
- Neighbor-Joining Tree
- Time series
- Visual analytics