Article

Selecting good views of high-dimensional data using class consistency

Authors:

Pat HanrahanAuthors Info & Claims

EuroVis'09: Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization

Pages 831 - 838

https://doi.org/10.1111/j.1467-8659.2009.01467.x

Published: 10 June 2009 Publication History

Abstract

Many visualization techniques involve mapping high-dimensional data spaces to lower-dimensional views. Unfortunately, mapping a high-dimensional data space into a scatterplot involves a loss of information; or, even worse, it can give a misleading picture of valuable structure in higher dimensions. In this paper, we propose class consistency as a measure of the quality of the mapping. Class consistency enforces the constraint that classes of n-D data are shown clearly in 2-D scatterplots. We propose two quantitative measures of class consistency, one based on the distance to the class's center of gravity, and another based on the entropies of the spatial distributions of classes. We performed an experiment where users choose good views, and show that class consistency has good precision and recall. We also evaluate both consistency measures over a range of data sets and show that these measures are efficient and robust.

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Jeon HKuo YAupetit MMa KSeo J(2024): Improving Label-Based Evaluation of Dimensionality ReductionIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332718730:1(781-791)Online publication date: 1-Jan-2024
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Index Terms

Selecting good views of high-dimensional data using class consistency
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Human-centered computing
  1. Human computer interaction (HCI)

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Information & Contributors

Information

Published In

cover image Guide Proceedings

EuroVis'09: Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization

June 2009

1054 pages

Editors:
Hans-Christian Hege
Zuse Institute Berlin, Germany
,
Ingrid Hotz
Zuse Institute Berlin, Germany
,
Tamara Munzner
University of British Columbia, Canada

Sponsors

ZIB: ZIB
IEEE VGTC: IEEE Visualization and Graphics Technical Committee
DFG Research Center Matheon: DFG Research Center Matheon
NVIDIA
EUROGRAPHICS: The European Association for Computer Graphics

Publisher

The Eurographs Association & John Wiley & Sons, Ltd.

Chichester, United Kingdom

Publication History

Published: 10 June 2009

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Cited By

Atzberger DCech TScheibel WDöllner JBehrisch MSchreck T(2024)A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text SpatializationsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345630831:1(305-315)Online publication date: 17-Sep-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3456308
Jeon HKuo YAupetit MMa KSeo J(2024): Improving Label-Based Evaluation of Dimensionality ReductionIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332718730:1(781-791)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3327187
Meng Lvan den Elzen SPezzotti NVilanova A(2024)Class-Constrained t-SNE: Combining Data Features and Class ProbabilitiesIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332660030:1(164-174)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3326600
Atzberger DCech TTrapp MRichter RScheibel WDöllner JSchreck T(2024)Large-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text SpatializationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332656930:1(902-912)Online publication date: 1-Jan-2024
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Telea AMachado AWang Y(2024)Seeing is Learning in High Dimensions: The Synergy Between Dimensionality Reduction and Machine LearningSN Computer Science10.1007/s42979-024-02604-y5:3Online publication date: 21-Feb-2024
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Wang QChen ZWang YQu H(2022)A Survey on ML4VIS: Applying Machine Learning Advances to Data VisualizationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.310614228:12(5134-5153)Online publication date: 1-Dec-2022
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Itoh TNakabayashi AHagita M(2022)Multidimensional data visualization applying a variety-oriented scatterplot selection techniqueJournal of Visualization10.1007/s12650-022-00871-626:1(199-210)Online publication date: 29-Aug-2022
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Itoh TNakabayashi AHagita M(2021)Scatterplot Selection Applying a Graph Coloring AlgorithmProceedings of the 14th International Symposium on Visual Information Communication and Interaction10.1145/3481549.3481553(1-6)Online publication date: 6-Sep-2021
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