@article{Sacha2018-01SOMFl-41125,
  year={2018},
  doi={10.1109/TVCG.2017.2744805},
  title={SOMFlow : Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance},
  number={1},
  volume={24},
  issn={1077-2626},
  journal={IEEE Transactions on Visualization and Computer Graphics},
  pages={120--130},
  author={Sacha, Dominik and Kraus, Matthias and Bernard, Jürgen and Behrisch, Michael and Schreck, Tobias and Asano, Yuki and Keim, Daniel A.}
}

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    <dc:contributor>Keim, Daniel A.</dc:contributor>
    <dc:creator>Bernard, Jürgen</dc:creator>
    <dc:creator>Keim, Daniel A.</dc:creator>
    <dc:contributor>Kraus, Matthias</dc:contributor>
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    <dcterms:title>SOMFlow : Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance</dcterms:title>
    <dc:contributor>Bernard, Jürgen</dc:contributor>
    <dc:contributor>Asano, Yuki</dc:contributor>
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    <dc:creator>Behrisch, Michael</dc:creator>
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    <dc:contributor>Schreck, Tobias</dc:contributor>
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    <dcterms:abstract xml:lang="eng">Clustering is a core building block for data analysis, aiming to extract otherwise hidden structures and relations from raw datasets, such as particular groups that can be effectively related, compared, and interpreted. A plethora of visual-interactive cluster analysis techniques has been proposed to date, however, arriving at useful clusterings often requires several rounds of user interactions to fine-tune the data preprocessing and algorithms. 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. It supports exploration by offering the analyst a visual platform to analyze intermediate results, adapt the underlying computations, iteratively partition the data, and to reflect previous analytical activities. The history of previous decisions is explicitly visualized within a flow graph, allowing to compare earlier cluster refinements and to explore relations. We further leverage quality and interestingness measures to guide the analyst in the discovery of useful patterns, relations, and data partitions. 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 understanding of clustering results as well as the interactive process itself.</dcterms:abstract>
    <dc:contributor>Sacha, Dominik</dc:contributor>
    <dc:contributor>Behrisch, Michael</dc:contributor>
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    <dc:creator>Sacha, Dominik</dc:creator>
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