@article{Schreck2009Visua-17389,
  year={2009},
  doi={10.1057/ivs.2008.29},
  title={Visual cluster analysis of trajectory data with interactive Kohonen maps},
  number={1},
  volume={8},
  issn={1473-8716},
  journal={Information Visualization},
  pages={14--29},
  author={Schreck, Tobias and Bernard, Jürgen and von Landesberger, Tatiana and Kohlhammer, Jörn}
}

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    <dc:contributor>Kohlhammer, Jörn</dc:contributor>
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    <dc:contributor>von Landesberger, Tatiana</dc:contributor>
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    <dcterms:title>Visual cluster analysis of trajectory data with interactive Kohonen maps</dcterms:title>
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    <dc:creator>von Landesberger, Tatiana</dc:creator>
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    <dc:contributor>Bernard, Jürgen</dc:contributor>
    <dc:contributor>Schreck, Tobias</dc:contributor>
    <dcterms:bibliographicCitation>First publ. in: Information Visualization ; 8 (2009), 1. - pp. 14-29</dcterms:bibliographicCitation>
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    <dcterms:abstract xml:lang="eng">Visual-interactive cluster analysis provides valuable tools for effectively analyzing large and complex data sets. Owing to desirable properties and an inherent predisposition for visualization, the Kohonen Feature Map (or Self-Organizing Map or SOM) algorithm is among the most popular and widely used visual clustering techniques. However, the unsupervised nature of the algorithm may be disadvantageous in certain applications. Depending on initialization and data characteristics, cluster maps (cluster layouts) may emerge that do not comply with user preferences, expectations or the application context. Considering SOM-based analysis of trajectory data, we propose a comprehensive visual-interactive monitoring and control framework extending the basic SOM algorithm. The framework implements the general Visual Analytics idea to effectively combine automatic data analysis with human expert supervision. It provides simple, yet effective facilities for visually monitoring and interactively controlling the trajectory clustering process at arbitrary levels of detail. The approach allows the user to leverage existing domain knowledge and user preferences, arriving at improved cluster maps. We apply the framework on several trajectory clustering problems, demonstrating its potential in combining both unsupervised (machine) and supervised (human expert) processing, in producing appropriate cluster results.</dcterms:abstract>
    <dc:creator>Schreck, Tobias</dc:creator>
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