ExplorerTree: A Focus+Context Exploration Approach for 2D Embeddings

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dc.contributor.authorMarcílio-Jr, Wilson E. [UNESP]
dc.contributor.authorEler, Danilo M. [UNESP]
dc.contributor.authorPaulovich, Fernando V.
dc.contributor.authorRodrigues-Jr, José F.
dc.contributor.authorArtero, Almir O. [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionDalhousie University
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.date.accessioned2022-04-28T19:40:22Z
dc.date.available2022-04-28T19:40:22Z
dc.date.issued2021-07-15
dc.description.abstractIn exploratory tasks involving high-dimensional datasets, dimensionality reduction (DR) techniques help analysts to discover patterns and other useful information. Although scatter plot representations of DR results allow for cluster identification and similarity analysis, such a visual metaphor presents problems when the number of instances of the dataset increases, resulting in cluttered visualizations. In this work, we propose a scatter plot-based multilevel approach to display DR results and address clutter-related problems when visualizing large datasets, together with the definition of a methodology to use focus+context interaction on non-hierarchical embeddings. The proposed technique, called ExplorerTree, uses a sampling selection technique on scatter plots to reduce visual clutter and guide users through exploratory tasks. We demonstrate ExplorerTree's effectiveness through a use case, where we visually explore activation images of the convolutional layers of a neural network. Finally, we also conducted a user experiment to evaluate ExplorerTree's ability to convey embedding structures using different sampling strategies.en
dc.description.affiliationFaculty of Sciences and Technology São Paulo State University (UNESP)
dc.description.affiliationFaculty of Computer Science Dalhousie University
dc.description.affiliationInstitute of Mathematics and Computer Sciences University of São Paulo
dc.description.affiliationUnespFaculty of Sciences and Technology São Paulo State University (UNESP)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipIdFAPESP: 2016/11707-6
dc.description.sponsorshipIdFAPESP: 2017/17450-0
dc.description.sponsorshipIdFAPESP: 2018/17881-3
dc.description.sponsorshipIdFAPESP: 2018/25755-8
dc.identifierhttp://dx.doi.org/10.1016/j.bdr.2021.100239
dc.identifier.citationBig Data Research, v. 25.
dc.identifier.doi10.1016/j.bdr.2021.100239
dc.identifier.issn2214-5796
dc.identifier.scopus2-s2.0-85107938654
dc.identifier.urihttp://hdl.handle.net/11449/221779
dc.language.isoeng
dc.relation.ispartofBig Data Research
dc.sourceScopus
dc.subjectDimensionality reduction
dc.subjectFocus+context
dc.subjectScatter-plot
dc.subjectVisualization
dc.titleExplorerTree: A Focus+Context Exploration Approach for 2D Embeddingsen
dc.typeArtigo
unesp.author.orcid0000-0002-8580-2779[1]
unesp.author.orcid0000-0002-9493-145X[2]
unesp.author.orcid0000-0002-2316-760X[3]
unesp.author.orcid0000-0001-8318-1780[4]

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