Skip to Main content Skip to Navigation
Conference papers

ProxiLens: Interactive Exploration of High-Dimensional Data using Projections

Nicolas Heulot 1, 2 Michael Aupetit 1 Jean-Daniel Fekete 2
2 AVIZ - Analysis and Visualization
Inria Saclay - Ile de France, LRI - Laboratoire de Recherche en Informatique, Université Paris-Saclay
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
Complete list of metadatas

Cited literature [18 references]  Display  Hide  Download

https://hal.inria.fr/hal-01523025
Contributor : Jean-Daniel Fekete <>
Submitted on : Sunday, October 8, 2017 - 9:26:20 AM
Last modification on : Wednesday, May 27, 2020 - 3:59:22 AM
Document(s) archivé(s) le : Tuesday, January 9, 2018 - 12:32:29 PM

File

proxilens.pdf
Publisher files allowed on an open archive

Identifiers

Collections

Citation

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, Michael Aupetit; Laurens van der Maaten, Jun 2013, Leipzig, Germany. ⟨10.2312/PE.VAMP.VAMP2013.011-015⟩. ⟨hal-01523025v2⟩

Share

Metrics

Record views

336

Files downloads

343