Guiding the exploration of scatter plot data using motif-based interest measures

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Journal of Visual Languages & Computing. 2016, 36, pp. 1-12. ISSN 1045-926X. eISSN 1095-8533. Available under: doi: 10.1016/j.jvlc.2016.07.003
Zusammenfassung

Finding interesting patterns in large scatter plot spaces is a challenging problem and becomes even more difficult with increasing number of dimensions. Previous approaches for exploring large scatter plot spaces like e.g., the well-known Scagnostics approach, mainly focus on ranking scatter plots based on their global properties. However, often local patterns contribute significantly to the interestingness of a scatter plot. We are proposing a novel approach for the automatic determination of interesting views in scatter plot spaces based on analysis of local scatter plot segments. Specifically, we automatically classify similar local scatter plot segments, which we call scatter plot motifs . Inspired by the well-known tf×idftf×idf-approach from information retrieval, we compute local and global quality measures based on frequency properties of the local motifs. We show how we can use these to filter, rank and compare scatter plots and their incorporated motifs. We demonstrate the usefulness of our approach with synthetic and real-world data sets and showcase our data exploration tools that visualize the distribution of local scatter plot motifs in relation to a large overall scatter plot space.

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ISO 690SHAO, Lin, Timo SCHLEICHER, Michael BEHRISCH, Tobias SCHRECK, Ivan SIPIRAN, Daniel A. KEIM, 2016. Guiding the exploration of scatter plot data using motif-based interest measures. In: Journal of Visual Languages & Computing. 2016, 36, pp. 1-12. ISSN 1045-926X. eISSN 1095-8533. Available under: doi: 10.1016/j.jvlc.2016.07.003
BibTex
@article{Shao2016Guidi-37436,
  year={2016},
  doi={10.1016/j.jvlc.2016.07.003},
  title={Guiding the exploration of scatter plot data using motif-based interest measures},
  volume={36},
  issn={1045-926X},
  journal={Journal of Visual Languages & Computing},
  pages={1--12},
  author={Shao, Lin and Schleicher, Timo and Behrisch, Michael and Schreck, Tobias and Sipiran, Ivan and Keim, Daniel A.}
}
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