A study on quality metrics vs. human perception: Can visual measures help us to filter visualizations of interest?
The number of visualizations being required for a complete view on data non-linearly grows with the number of data dimensions. Thus, relevant visualizations need to be filtered to guide the user during the visual search. A popular filter approach is the usage of quality metrics, which map a visual p...
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Published in | Information technology (Munich, Germany) Vol. 57; no. 1; pp. 11 - 21 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
De Gruyter Oldenbourg
28.02.2015
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Subjects | |
Online Access | Get full text |
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Summary: | The number of visualizations being required for a complete view on
data non-linearly grows with the number of data dimensions. Thus,
relevant visualizations need to be filtered to guide the user during
the visual search. A popular filter approach is the usage of quality
metrics, which map a visual pattern to a real number. This way,
visualizations that contain interesting patterns are
automatically detected. Quality metrics are
a useful tool in visual analysis, if they resemble the human
perception. In this work we present a broad study to examine the
relation between filtering relevant visualizations based on human
perception versus quality metrics. For this, seven
widely-used quality metrics were tested on five
high-dimensional datasets, covering scatterplots, parallel
coordinates, and radial visualizations. In total, 102 participants
were available. The results of our studies show that quality
metrics often work similar to the human perception. Interestingly,
a subset of so-called Scagnostic measures does the best job. |
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ISSN: | 1611-2776 2196-7032 |
DOI: | 10.1515/itit-2014-1070 |