ProxiLens: Interactive Exploration of High-Dimensional Data using Projections

Abstract : As 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.
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Communication dans un congrès
VAMP: EuroVis Workshop on Visual Analytics using Multidimensional Projections, Jun 2013, Leipzig, Germany. The Eurographics Association, 2013, 〈https://diglib.eg.org/handle/10.2312/1002〉. 〈10.2312/PE.VAMP.VAMP2013.011-015〉
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Soumis le : dimanche 8 octobre 2017 - 09:26:20
Dernière modification le : mardi 10 octobre 2017 - 13:47:22

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Nicolas Heulot, Michael Aupetit, Jean-Daniel Fekete. ProxiLens: Interactive Exploration of High-Dimensional Data using Projections. VAMP: EuroVis Workshop on Visual Analytics using Multidimensional Projections, Jun 2013, Leipzig, Germany. The Eurographics Association, 2013, 〈https://diglib.eg.org/handle/10.2312/1002〉. 〈10.2312/PE.VAMP.VAMP2013.011-015〉. 〈hal-01523025v2〉

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