ProxiLens: Interactive Exploration of High-Dimensional Data using Projections

dc.contributor.authorHeulot, N.en_US
dc.contributor.authorAupetit, M.en_US
dc.contributor.authorFekete, J-D.en_US
dc.contributor.editorM. Aupetit and L. van der Maatenen_US
dc.date.accessioned2014-02-01T15:50:31Z
dc.date.available2014-02-01T15:50:31Z
dc.date.issued2013en_US
dc.description.abstractAs dimensionality increases, analysts are faced with difficult problems to make sense of their data. In exploratory data analysis, multidimensional scaling projections can help analyst to discover patterns by identifying outliers and enabling visual clustering. However to exploit these projections, artifacts and interpretation issues must be overcome. We present ProxiLens, a semantic lens which helps exploring data interactively. The analyst becomes aware of the artifacts navigating in a continuous way through the 2D projection in order to cluster and analyze data. We demonstrate the applicability of our technique for visual clustering on synthetic and real data sets.en_US
dc.description.seriesinformationEuroVis Workshop on Visual Analytics using Multidimensional Projectionsen_US
dc.identifier.isbn978-3-905674-53-8en_US
dc.identifier.urihttps://doi.org/10.2312/PE.VAMP.VAMP2013.011-015en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectH.5.2 [Information Interfaces and Presentation]en_US
dc.subjectUser Interfacesen_US
dc.titleProxiLens: Interactive Exploration of High-Dimensional Data using Projectionsen_US
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