%0 Conference Paper %1 conf/ieeevast/Blumenschein0SB18 %A Blumenschein, Michael %A Behrisch, Michael %A Schmid, Stefanie %A Butscher, Simon %A Wahl, Deborah R. %A Villinger, Karoline %A Renner, Britta %A Reiterer, Harald %A Keim, Daniel A. %B Proceedings of the IEEE Conference on Visual Analytics Science and Technology (VAST) %D 2018 %E Chang, Remco %E Qu, Huamin %E Schreck, Tobias %I IEEE %K 2018 A03 C01 from:leonkokkoliadis sfbtrr161 %P 36-47 %R 10.1109/VAST.2018.8802486 %T SMARTexplore: Simplifying High-Dimensional Data Analysis through a Table-Based Visual Analytics Approach %U https://ieeexplore.ieee.org/document/8802486 %X We present SMARTEXPLORE, a novel visual analytics technique that simplifies the identification and understanding of clusters, correlations, and complex patterns in high-dimensional data. The analysis is integrated into an interactive table-based visualization that maintains a consistent and familiar representation throughout the analysis. The visualization is tightly coupled with pattern matching, subspace analysis, reordering, and layout algorithms. To increase the analyst's trust in the revealed patterns, SMARTEXPLORE automatically selects and computes statistical measures based on dimension and data properties. While existing approaches to analyzing high-dimensional data (e.g., planar projections and Parallel coordinates) have proven effective, they typically have steep learning curves for non-visualization experts. Our evaluation, based on three expert case studies, confirms that non-visualization experts successfully reveal patterns in high-dimensional data when using SMARTEXPLORE

@inproceedings{conf/ieeevast/Blumenschein0SB18, abstract = {We present SMARTEXPLORE, a novel visual analytics technique that simplifies the identification and understanding of clusters, correlations, and complex patterns in high-dimensional data. The analysis is integrated into an interactive table-based visualization that maintains a consistent and familiar representation throughout the analysis. The visualization is tightly coupled with pattern matching, subspace analysis, reordering, and layout algorithms. To increase the analyst's trust in the revealed patterns, SMARTEXPLORE automatically selects and computes statistical measures based on dimension and data properties. While existing approaches to analyzing high-dimensional data (e.g., planar projections and Parallel coordinates) have proven effective, they typically have steep learning curves for non-visualization experts. Our evaluation, based on three expert case studies, confirms that non-visualization experts successfully reveal patterns in high-dimensional data when using SMARTEXPLORE}, added-at = {2020-01-30T15:53:42.000+0100}, author = {Blumenschein, Michael and Behrisch, Michael and Schmid, Stefanie and Butscher, Simon and Wahl, Deborah R. and Villinger, Karoline and Renner, Britta and Reiterer, Harald and Keim, Daniel A.}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2fb1c5864833f686b54bb63063704a1e1/sfbtrr161}, booktitle = {Proceedings of the IEEE Conference on Visual Analytics Science and Technology (VAST)}, crossref = {conf/ieeevast/2018}, doi = {10.1109/VAST.2018.8802486}, editor = {Chang, Remco and Qu, Huamin and Schreck, Tobias}, ee = {https://doi.org/10.1109/VAST.2018.8802486}, interhash = {b99246d010bf99c1eba6a2fd60a66038}, intrahash = {fb1c5864833f686b54bb63063704a1e1}, keywords = {2018 A03 C01 from:leonkokkoliadis sfbtrr161}, pages = {36-47}, publisher = {IEEE}, timestamp = {2020-03-27T15:45:26.000+0100}, title = {SMARTexplore: Simplifying High-Dimensional Data Analysis through a Table-Based Visual Analytics Approach}, url = {https://ieeexplore.ieee.org/document/8802486}, year = 2018 }