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

Descripción del Articulo

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.
Detalles Bibliográficos
Autores: Rodríguez R., Alfonte R., Cuadros A.M.
Formato: objeto de conferencia
Fecha de Publicación:2018
Institución:Consejo Nacional de Ciencia Tecnología e Innovación
Repositorio:CONCYTEC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.concytec.gob.pe:20.500.12390/528
Enlace del recurso:https://hdl.handle.net/20.500.12390/528
https://doi.org/10.1145/3177457.3177466
Nivel de acceso:acceso abierto
Materia: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
https://purl.org/pe-repo/ocde/ford#2.02.04
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oai_identifier_str oai:repositorio.concytec.gob.pe:20.500.12390/528
network_acronym_str CONC
network_name_str CONCYTEC-Institucional
repository_id_str 4689
dc.title.none.fl_str_mv A visual analytics approach for exploration of high-dimensional time series based on Neighbor-Joining Tree
title A visual analytics approach for exploration of high-dimensional time series based on Neighbor-Joining Tree
spellingShingle A visual analytics approach for exploration of high-dimensional time series based on Neighbor-Joining Tree
Rodríguez R.
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
https://purl.org/pe-repo/ocde/ford#2.02.04
title_short A visual analytics approach for exploration of high-dimensional time series based on Neighbor-Joining Tree
title_full A visual analytics approach for exploration of high-dimensional time series based on Neighbor-Joining Tree
title_fullStr A visual analytics approach for exploration of high-dimensional time series based on Neighbor-Joining Tree
title_full_unstemmed A visual analytics approach for exploration of high-dimensional time series based on Neighbor-Joining Tree
title_sort A visual analytics approach for exploration of high-dimensional time series based on Neighbor-Joining Tree
author Rodríguez R.
author_facet Rodríguez R.
Alfonte R.
Cuadros A.M.
author_role author
author2 Alfonte R.
Cuadros A.M.
author2_role author
author
dc.contributor.author.fl_str_mv Rodríguez R.
Alfonte R.
Cuadros A.M.
dc.subject.none.fl_str_mv Visualization
topic 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
https://purl.org/pe-repo/ocde/ford#2.02.04
dc.subject.es_PE.fl_str_mv 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
dc.subject.ocde.none.fl_str_mv https://purl.org/pe-repo/ocde/ford#2.02.04
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.
publishDate 2018
dc.date.accessioned.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.available.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.issued.fl_str_mv 2018
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
dc.identifier.isbn.none.fl_str_mv 9781450363396
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12390/528
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1145/3177457.3177466
dc.identifier.scopus.none.fl_str_mv 2-s2.0-85049863164
identifier_str_mv 9781450363396
2-s2.0-85049863164
url https://hdl.handle.net/20.500.12390/528
https://doi.org/10.1145/3177457.3177466
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.none.fl_str_mv ACM International Conference Proceeding Series
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Association for Computing Machinery
publisher.none.fl_str_mv Association for Computing Machinery
dc.source.none.fl_str_mv reponame:CONCYTEC-Institucional
instname:Consejo Nacional de Ciencia Tecnología e Innovación
instacron:CONCYTEC
instname_str Consejo Nacional de Ciencia Tecnología e Innovación
instacron_str CONCYTEC
institution CONCYTEC
reponame_str CONCYTEC-Institucional
collection CONCYTEC-Institucional
repository.name.fl_str_mv Repositorio Institucional CONCYTEC
repository.mail.fl_str_mv repositorio@concytec.gob.pe
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spelling Publicationrp00879600rp00881600rp00880600Rodríguez R.Alfonte R.Cuadros A.M.2024-05-30T23:13:38Z2024-05-30T23:13:38Z20189781450363396https://hdl.handle.net/20.500.12390/528https://doi.org/10.1145/3177457.31774662-s2.0-85049863164The 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.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.Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - ConcytecengAssociation for Computing MachineryACM International Conference Proceeding Seriesinfo:eu-repo/semantics/openAccessVisualizationData visualization-1Forestry-1Time series-1Dimension reduction techniques-1Exploratory analysis-1High-dimensional-1Interaction mechanisms-1Neighbor joining-1Similarity measure-1Visual analytics-1Visual techniques-1Time series analysis-1https://purl.org/pe-repo/ocde/ford#2.02.04-1A visual analytics approach for exploration of high-dimensional time series based on Neighbor-Joining Treeinfo:eu-repo/semantics/conferenceObjectreponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTEC#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#20.500.12390/528oai:repositorio.concytec.gob.pe:20.500.12390/5282024-05-30 15:35:39.255http://purl.org/coar/access_right/c_14cbinfo:eu-repo/semantics/closedAccessmetadata only accesshttps://repositorio.concytec.gob.peRepositorio Institucional CONCYTECrepositorio@concytec.gob.pe#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="61512c2c-14f4-4336-8b2a-e0f79cf0827e"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>A visual analytics approach for exploration of high-dimensional time series based on Neighbor-Joining Tree</Title> <PublishedIn> <Publication> <Title>ACM International Conference Proceeding Series</Title> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <DOI>https://doi.org/10.1145/3177457.3177466</DOI> <SCP-Number>2-s2.0-85049863164</SCP-Number> <ISBN>9781450363396</ISBN> <Authors> <Author> <DisplayName>Rodríguez R.</DisplayName> <Person id="rp00879" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Alfonte R.</DisplayName> <Person id="rp00881" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Cuadros A.M.</DisplayName> <Person id="rp00880" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Association for Computing Machinery</DisplayName> <OrgUnit /> </Publisher> </Publishers> <Keyword>Visualization</Keyword> <Keyword>Data visualization</Keyword> <Keyword>Forestry</Keyword> <Keyword>Time series</Keyword> <Keyword>Dimension reduction techniques</Keyword> <Keyword>Exploratory analysis</Keyword> <Keyword>High-dimensional</Keyword> <Keyword>Interaction mechanisms</Keyword> <Keyword>Neighbor joining</Keyword> <Keyword>Similarity measure</Keyword> <Keyword>Visual analytics</Keyword> <Keyword>Visual techniques</Keyword> <Keyword>Time series analysis</Keyword> <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.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1
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A visual analytics approach for exploration of high-dimensional time series based on Neighbor-Joining Tree
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La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).

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