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User Similarity and Deviation Analysis for Adaptive Visualizations

  • Kawa Nazemi
  • Wilhelm Retz
  • Jörn Kohlhammer
  • Arjan Kuijper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8521)

Abstract

Adaptive visualizations support users in information acquisition and exploration and therewith in human access of data. Their adaptation effect is often based on approaches that require the training by an expert. Further the effects often aims to support just the individual aptitudes. This paper introduces an approach for modeling a canonical user that makes the predefined training-files dispensable and enables an adaptation of visualizations for the majority of users. With the introduced user deviation algorithm, the behavior of individuals can be compared to the average user behavior represented in the canonical user model to identify behavioral anomalies. The further introduced similarity measurements allow to cluster similar deviated behavioral patterns as groups and provide them effective visual adaptations.

Keywords

User Group Data Element User Model Individual User Deviation Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kawa Nazemi
    • 1
    • 2
  • Wilhelm Retz
    • 1
  • Jörn Kohlhammer
    • 1
    • 2
  • Arjan Kuijper
    • 1
    • 2
  1. 1.Fraunhofer IGDDarmstadtGermany
  2. 2.Technische Universität DarmstadtDarmstadtGermany

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