Improving the Robustness of Scagnostics

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WANG, Yunhai, Zeyu WANG, Tingting LIU, Michael CORRELL, Zhanglin CHENG, Oliver DEUSSEN, Michael SEDLMAIR, 2020. Improving the Robustness of Scagnostics. In: IEEE Transactions on Visualization and Computer Graphics. Institute of Electrical and Electronics Engineers (IEEE). 26(1), pp. 759-769. ISSN 1077-2626. eISSN 1941-0506. Available under: doi: 10.1109/TVCG.2019.2934796

@article{Wang2020-01Impro-47041, title={Improving the Robustness of Scagnostics}, year={2020}, doi={10.1109/TVCG.2019.2934796}, number={1}, volume={26}, issn={1077-2626}, journal={IEEE Transactions on Visualization and Computer Graphics}, pages={759--769}, author={Wang, Yunhai and Wang, Zeyu and Liu, Tingting and Correll, Michael and Cheng, Zhanglin and Deussen, Oliver and Sedlmair, Michael} }

<rdf:RDF xmlns:dcterms="" xmlns:dc="" xmlns:rdf="" xmlns:bibo="" xmlns:dspace="" xmlns:foaf="" xmlns:void="" xmlns:xsd="" > <rdf:Description rdf:about=""> <dc:creator>Wang, Zeyu</dc:creator> <dc:creator>Wang, Yunhai</dc:creator> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dc:contributor>Cheng, Zhanglin</dc:contributor> <dc:contributor>Wang, Zeyu</dc:contributor> <dc:contributor>Sedlmair, Michael</dc:contributor> <dc:creator>Deussen, Oliver</dc:creator> <dcterms:available rdf:datatype="">2019-09-26T12:57:16Z</dcterms:available> <dcterms:isPartOf rdf:resource=""/> <dc:creator>Liu, Tingting</dc:creator> <dspace:isPartOfCollection rdf:resource=""/> <dc:creator>Sedlmair, Michael</dc:creator> <dcterms:issued>2020-01</dcterms:issued> <dc:contributor>Liu, Tingting</dc:contributor> <dc:language>eng</dc:language> <dcterms:title>Improving the Robustness of Scagnostics</dcterms:title> <dc:contributor>Deussen, Oliver</dc:contributor> <dc:contributor>Correll, Michael</dc:contributor> <dcterms:abstract xml:lang="eng">In this paper, we examine the robustness of scagnostics through a series of theoretical and empirical studies. First, we investigate the sensitivity of scagnostics by employing perturbing operations on more than 60M synthetic and real-world scatterplots. We found that two scagnostic measures, Outlying and Clumpy, are overly sensitive to data binning. To understand how these measures align with human judgments of visual features, we conducted a study with 24 participants, which reveals that i) humans are not sensitive to small perturbations of the data that cause large changes in both measures, and ii) the perception of clumpiness heavily depends on per-cluster topologies and structures. Motivated by these results, we propose Robust Scagnostics (RScag) by combining adaptive binning with a hierarchy-based form of scagnostics. An analysis shows that RScag improves on the robustness of original scagnostics, aligns better with human judgments, and is equally fast as the traditional scagnostic measures.</dcterms:abstract> <dc:date rdf:datatype="">2019-09-26T12:57:16Z</dc:date> <dc:contributor>Wang, Yunhai</dc:contributor> <bibo:uri rdf:resource=""/> <dc:creator>Correll, Michael</dc:creator> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:creator>Cheng, Zhanglin</dc:creator> </rdf:Description> </rdf:RDF>

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