Publication:
A visual analytics approach for exploration of high-dimensional time series based on Neighbor-Joining Tree
A visual analytics approach for exploration of high-dimensional time series based on Neighbor-Joining Tree
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Date
2018
Authors
Rodríguez R.
Alfonte R.
Cuadros A.M.
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Publisher
Association for Computing Machinery
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Abstract
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.
Description
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.
Keywords
Visualization,
Data visualization,
Forestry,
Time series,
Dimension reduction techniques,
Exploratory analysis,
High-dimensional,
Interaction mechanisms,
Neighbor joining,
Similarity measure,
Visual analytics,
Visual techniques,
Time series analysis