Publication:
A Visual Analytics Approach for Exploration of High-Dimensional Time Series Based on Neighbor-Joining Tree Actualizado
A Visual Analytics Approach for Exploration of High-Dimensional Time Series Based on Neighbor-Joining Tree Actualizado
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Date
2018-03-02
Authors
Roberto Rodríguez Urquiaga
Reynaldo Alfonte Zapana
Ana Maria Cuadros Valdivia
Journal Title
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Volume Title
Publisher
Association for Computing Machinery 333
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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 Actualizado.
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. © 2018 Association for Computing Machinery.
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. © 2018 Association for Computing Machinery.
Description
Keywords
Bioinformatics,
Data visualization,
Forestry,
Joining,
Time series,
Visualization,
Dimension reduction techniques,
Exploratory analysis,
Interaction mechanisms,
Neighbor joining,
Similarity measure,
Visual analytics,
Visual techniques,
Time series analysis