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Towards a user-defined visual-interactive definition of similarity functions for mixed data

: Bernard, Jürgen; Hutter, Marco; Sessler, David; Schreck, Tobias; Behrisch, Michael; Kohlhammer, Jörn


Chen, M. ; Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
IEEE Conference on Visual Analytics Science and Technology, VAST 2014. Proceedings : 9–14 November 2014, Paris, France
Piscataway, NJ: IEEE, 2014
ISBN: 978-1-4799-6227-3
ISBN: 978-1-4799-6186-3
Conference on Visual Analytics Science and Technology (VAST) <9, 2014, Paris>
Conference Paper
Fraunhofer IGD ()
Fraunhofer IGD-R ()
visual analytic; information visualization; similarity measure; similarity metric; similarity search; Business Field: Visual decision support; Business Field: Digital society; Research Area: Computer vision (CV); Research Area: Human computer interaction (HCI)

The creation of similarity functions based on visual-interactive user feedback is a promising means to capture the mental similarity notion in the heads of domain experts. In particular, concepts exist where users arrange multivariate data objects on a 2D data landscape in order to learn new similarity functions. While systems that incorporate numerical data attributes have been presented in the past, the remaining overall goal may be to develop systems also for mixed data sets. In this work, we present a feedback model for categorical data which can be used alongside of numerical feedback models in future.