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

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Fecha
2018
Autores
Rodríguez R.
Alfonte R.
Cuadros A.M.
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Association for Computing Machinery
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Abstracto
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.
Descripción
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.
Palabras clave
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
Citación