Evaluating Differences in Insights from Interactive Dimensionality Reduction Visualizations Through Complexity and Vocabulary

Mia Taylor; Lata Kodali; Leanna House; Chris North

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Paper

Evaluating Differences in Insights from Interactive Dimensionality Reduction Visualizations Through Complexity and Vocabulary

Topics: High-Dimensional Data and Dimensionality Reduction; Interpretation and Evaluation Methods; Multivariate Data Visualisation; Usability Studies and Visualization; Visual Analytics

In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISIGRAPP, 158-165, 2023 , Lisbon, Portugal

Authors: Mia Taylor ¹ ; Lata Kodali ² ; Leanna House ² and Chris North ¹

Affiliations: ¹ Department of Computer Science, Virginia Tech, U.S.A. ; ² Department of Statistics, Virginia Tech, U.S.A.

Keyword(s): Visualization, Dimensionality Reduction, Logistic Regression, Applied Natural Language Processing.

Abstract: The software, Andromeda, enables users to explore high-dimensional data using the dimensionality reduction algorithm Weighted Multidimensional Scaling (WMDS). How data are projected in WMDS is determined by weights assigned to variables, and with Andromeda, the weights are set in response to user interactions. This work evaluates the impact of such interactions on student insight generation via a large-scale study implemented in a university introductory statistics course. Insights are analyzed using complexity metrics. This analysis is extended to compare insight vocabulary to gain an understanding of differences in terminology. Both analyses are conducted using the same semi-automated method that applies basic natural language processing techniques and logistic regression modeling. Results show that specific user interactions correlate to differences in the dimensionality and cardinality of insights. Overall, these results suggest that the interactions available to users impact the ir insight generation and therefore impact their learning and analysis process. (More)

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Paper citation in several formats:

Taylor, M., Kodali, L., House, L. and North, C. (2023). Evaluating Differences in Insights from Interactive Dimensionality Reduction Visualizations Through Complexity and Vocabulary. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - IVAPP; ISBN 978-989-758-634-7; ISSN 2184-4321, SciTePress, pages 158-165. DOI: 10.5220/0011663500003417

@conference{ivapp23,
author={Mia Taylor and Lata Kodali and Leanna House and Chris North},
title={Evaluating Differences in Insights from Interactive Dimensionality Reduction Visualizations Through Complexity and Vocabulary},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - IVAPP},
year={2023},
pages={158-165},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011663500003417},
isbn={978-989-758-634-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - IVAPP
TI - Evaluating Differences in Insights from Interactive Dimensionality Reduction Visualizations Through Complexity and Vocabulary
SN - 978-989-758-634-7
IS - 2184-4321
AU - Taylor, M.
AU - Kodali, L.
AU - House, L.
AU - North, C.
PY - 2023
SP - 158
EP - 165
DO - 10.5220/0011663500003417
PB - SciTePress