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Mining Networks through Visual Analytics: Incremental Hypothesis Building and Validation

David Auber 1, 2 Guy Melançon 2, 3 Romain Bourqui 1, 2 
2 GRAVITE - Graph Visualization and Interactive Exploration
Université Sciences et Technologies - Bordeaux 1, Inria Bordeaux - Sud-Ouest, École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), CNRS - Centre National de la Recherche Scientifique : UMR
3 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Interactive visualization supports the analytical process: interacting with abstract views, using the data as a malleable material, analysts build hypothesis that can be further validated on the whole dataset. We use graph clustering in order to group elements and show them as meta-nodes and reduce the number of visual items, further organizing data over into several layers, in an effort to provide a multilevel model of the studied phenomenon. In doing so, hierarchical clustering not only contributes to the study of data but also brings in ingredients for interaction, enabling the user to zoom in and out of clusters while exploring the data in quest for evidence.
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Contributor : Guy Melançon Connect in order to contact the contributor
Submitted on : Friday, April 11, 2008 - 5:47:51 PM
Last modification on : Saturday, June 25, 2022 - 8:29:35 PM


  • HAL Id : lirmm-00272781, version 1


David Auber, Guy Melançon, Romain Bourqui. Mining Networks through Visual Analytics: Incremental Hypothesis Building and Validation. Clive Best, Françoise Fogelman Soulié. Mining Massive Data Sets for Security, IOS Press, pp.204-211, 2008, NATO Advanced Study Institute, 978-1-58603-898-4. ⟨lirmm-00272781⟩



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