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Towards a User-Defined Visual-Interactive Definition of Similarity Functions for Mixed Data

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2014

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Bernard, Jürgen
Sessler, David
Hutter, Marco
Kohlhammer, Jorn

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https://doi.org/10.1109/VAST.2014.7042503
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MIN CHEN ..., , ed.. 2014 IEEE Conference on Visual Analytics Science and Technology, Paris, France, 9-14 October 2014, Proceedings. Piscataway, NJ: IEEE, 2014, pp. 227-228. ISBN 978-1-4799-6227-3. Available under: doi: 10.1109/VAST.2014.7042503

Zusammenfassung

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.

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004 Informatik

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IEEE Conference on Visual Analytics Science and Technology (VAST), 2014, 9. Okt. 2014 - 14. Okt. 2014, Paris
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ISO 690BERNARD, Jürgen, David SESSLER, Michael BEHRISCH, Marco HUTTER, Tobias SCHRECK, Jorn KOHLHAMMER, 2014. Towards a User-Defined Visual-Interactive Definition of Similarity Functions for Mixed Data. IEEE Conference on Visual Analytics Science and Technology (VAST), 2014. Paris, 9. Okt. 2014 - 14. Okt. 2014. In: MIN CHEN ..., , ed.. 2014 IEEE Conference on Visual Analytics Science and Technology, Paris, France, 9-14 October 2014, Proceedings. Piscataway, NJ: IEEE, 2014, pp. 227-228. ISBN 978-1-4799-6227-3. Available under: doi: 10.1109/VAST.2014.7042503
BibTex
@inproceedings{Bernard2014Towar-30005,
  year={2014},
  doi={10.1109/VAST.2014.7042503},
  title={Towards a User-Defined Visual-Interactive Definition of Similarity Functions for Mixed Data},
  isbn={978-1-4799-6227-3},
  publisher={IEEE},
  address={Piscataway, NJ},
  booktitle={2014 IEEE Conference on Visual Analytics Science and Technology, Paris, France, 9-14 October 2014, Proceedings},
  pages={227--228},
  editor={Min Chen ...},
  author={Bernard, Jürgen and Sessler, David and Behrisch, Michael and Hutter, Marco and Schreck, Tobias and Kohlhammer, Jorn}
}
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