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

Roberto Rodríguez, Reynaldo Alfonte, Ana María Cuadros Valdivia

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 wellknown similarity measures and interaction mechanisms to do exploratory analysis of high-dimensional time series data interactively.

Original languageEnglish
Title of host publicationProceedings of the 10th International Conference on Computer Modeling and Simulation, ICCMS 2018
PublisherAssociation for Computing Machinery
Pages44-48
Number of pages5
ISBN (Electronic)9781450363396
DOIs
StatePublished - 8 Jan 2018
Externally publishedYes
Event10th International Conference on Computer Modeling and Simulation, ICCMS 2018 - Sydney, Australia
Duration: 8 Jan 201810 Jan 2018

Publication series

NameACM International Conference Proceeding Series

Conference

Conference10th International Conference on Computer Modeling and Simulation, ICCMS 2018
Country/TerritoryAustralia
CitySydney
Period8/01/1810/01/18

Bibliographical note

Funding 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.

Publisher Copyright:
© 2018 Association for Computing Machinery.

Keywords

  • High dimensional
  • Neighbor-Joining Tree
  • Time series
  • Visual analytics

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