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SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance
2018
IEEE Transactions on Visualization and Computer Graphics
Preserved Fulltext
We present a multi-stage Visual Analytics (VA) approach for iterative cluster refinement together with an implementation (SOMFlow) that uses Self-Organizing Maps (SOM) to analyze time series data. ...
We conducted two pair analytics experiments together with a subject matter expert in speech intonation research to demonstrate that the approach is effective for interactive data analysis, supporting enhanced ...
BACKGROUND ON SELF-ORGANIZING MAPS We chose the SOM algorithm (also known as Kohonen Maps [31] ) as a fundamental approach to automatically generate data overviews. ...
doi:10.1109/tvcg.2017.2744805
pmid:28866559
fatcat:3gyhpgwbrnes7b7biyix3kaeti
2018 Index IEEE Transactions on Visualization and Computer Graphics Vol. 24
2019
IEEE Transactions on Visualization and Computer Graphics
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
June 2018 2023-2036 Summarization and Stage Analysis of Event Sequence Data; TVCG Jan. 2018 56-65 Guo, Y., see Liu, L., TVCG June 2018 1956-1968 ...
., and Dai, Q., Errata to "Robust Non-Rigid Motion Tracking and Surface Reconstruction Using L 0 Regularization"; TVCG July 2018 2268 Guo, K., Xu, F., Wang, Y., Liu, Y., and Dai, Q., Robust Non-Rigid ...
.,
þ, TVCG Jan. 2018 246-255
SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing
Maps and Analytic Provenance. ...
doi:10.1109/tvcg.2018.2885445
fatcat:xtccdnalwfhudp3qdxofz4kqpi