ProxiLens: Interactive Exploration of High-Dimensional Data using Projections

Nicolas Heulot; Michael Aupetit; Jean-Daniel Fekete

ProxiLens: Interactive Exploration of High-Dimensional Data using Projections

Nicolas Heulot ^{1, 2} Michael Aupetit ¹ Jean-Daniel Fekete ²

1 DISC - Département Imagerie et Simulation pour le Contrôle

Inria Saclay - Ile de France

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.

Document type :

Conference papers

Domain :

Computer Science [cs] / Human-Computer Interaction [cs.HC]

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https://hal.inria.fr/hal-01523025
Contributor : Jean-Daniel Fekete <>
Submitted on : Sunday, October 8, 2017 - 9:26:20 AM
Last modification on : Thursday, February 14, 2019 - 5:20:03 PM
Long-term archiving on : Tuesday, January 9, 2018 - 12:32:29 PM

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