Publikation:

FS/DS : A Theoretical Framework for the Dual Analysis of Feature Space and Data Space

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2024

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oops

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Deutsche Forschungsgemeinschaft (DFG): 251654672
Deutsche Forschungsgemeinschaft (DFG): 251654672 – TRR 161 (Project A03)

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IEEE Transactions on Visualization and Computer Graphics. IEEE. 2024, 30(8), S. 5165-5182. ISSN 1077-2626. eISSN 1941-0506. Verfügbar unter: doi: 10.1109/tvcg.2023.3288356

Zusammenfassung

With the surge of data-driven analysis techniques, there is a rising demand for enhancing the exploration of large high-dimensional data by enabling interactions for the joint analysis of features (i.e., dimensions). Such a dual analysis of the feature space and data space is characterized by three components, (1) a view visualizing feature summaries, (2) a view that visualizes the data records, and (3) a bidirectional linking of both plots triggered by human interaction in one of both visualizations, e.g., Linking & Brushing. Dual analysis approaches span many domains, e.g., medicine, crime analysis, and biology. The proposed solutions encapsulate various techniques, such as feature selection or statistical analysis. However, each approach establishes a new definition of dual analysis. To address this gap, we systematically reviewed published dual analysis methods to investigate and formalize the key elements, such as the techniques used to visualize the feature space and data space, as well as the interaction between both spaces. From the information elicited during our review, we propose a unified theoretical framework for dual analysis, encompassing all existing approaches extending the field. We apply our proposed formalization describing the interactions between each component and relate them to the addressed tasks. Additionally, we categorize the existing approaches using our framework and derive future research directions to advance dual analysis by including state-of-the-art visual analysis techniques to improve data exploration.

Zusammenfassung in einer weiteren Sprache

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004 Informatik

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Visual analytics, dual analysis, feature space, data space, feature exploration, mixed data, high-dimensional data

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ISO 690DENNIG, Frederik L., Matthias MILLER, Daniel A. KEIM, Mennatallah EL-ASSADY, 2024. FS/DS : A Theoretical Framework for the Dual Analysis of Feature Space and Data Space. In: IEEE Transactions on Visualization and Computer Graphics. IEEE. 2024, 30(8), S. 5165-5182. ISSN 1077-2626. eISSN 1941-0506. Verfügbar unter: doi: 10.1109/tvcg.2023.3288356
BibTex
@article{Dennig2024-08Theor-67492,
  year={2024},
  doi={10.1109/tvcg.2023.3288356},
  title={FS/DS : A Theoretical Framework for the Dual Analysis of Feature Space and Data Space},
  number={8},
  volume={30},
  issn={1077-2626},
  journal={IEEE Transactions on Visualization and Computer Graphics},
  pages={5165--5182},
  author={Dennig, Frederik L. and Miller, Matthias and Keim, Daniel A. and El-Assady, Mennatallah}
}
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