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Histogram equalization and specification for high-dimensional data visualization using RadViz

Authors:

Hock-Soon SeahAuthors Info & Claims

CGI '17: Proceedings of the Computer Graphics International Conference

Article No.: 15, Pages 1 - 6

https://doi.org/10.1145/3095140.3095155

Published: 27 June 2017 Publication History

Abstract

In our turbine performance assessment, we need to provide an effective visual analytics tool in handling high-dimensional datasets. We have employed RadViz in 2D exploratory data analysis. However, with the increase of dataset size and dimensionality, the clumping of projected data points towards the origin in RadViz causes low space utilization, which largely degenerates the visibility of the feature characteristics. In this study, to better evaluate the hidden patterns in the center region, we propose histogram-based techniques to manipulate the radial distribution of data points in RadViz. We present RadViz in the polar coordinate system for convenient radial operations. Based on this, we define the radial equalization method to automatically spread out the frequency and the radial specification method to shape the distribution based on the user's requirement. Furthermore, we utilize the information in high-dimensional space as histogram and reference point to design and control the radial distribution of RadViz. Computational experiments have been conducted on turbine performance simulation data. Our proposed techniques are shown advantageous in query result display and outlier detection with a set of high-dimensional datasets.

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Cited By

Angelini MBlasilli GLenti SPalleschi ASantucci G(2022)Effectiveness Error: Measuring and Improving RadViz Visual EffectivenessIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.310487928:12(4770-4786)Online publication date: 1-Dec-2022
https://doi.org/10.1109/TVCG.2021.3104879
Zhang CTan KLi HHong G(2019)A Cost-Sensitive Deep Belief Network for Imbalanced ClassificationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2018.283264830:1(109-122)Online publication date: Jan-2019
https://doi.org/10.1109/TNNLS.2018.2832648
Seyam SAl-Hamed KQureshy ADincer IAgelin-Chaab MRahnamayan S(2019)Multi-objective Optimization of Hydrogen Production in Hybrid Renewable Energy Systems2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8790299(850-857)Online publication date: Jun-2019
https://doi.org/10.1109/CEC.2019.8790299
Show More Cited By

Index Terms

Histogram equalization and specification for high-dimensional data visualization using RadViz
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Information visualization
      2. Visual analytics
    2. Visualization theory, concepts and paradigms

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Information & Contributors

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Published In

cover image ACM Other conferences

CGI '17: Proceedings of the Computer Graphics International Conference

June 2017

260 pages

ISBN:9781450352284

DOI:10.1145/3095140

Program Chairs:
Xiaoyang Mao
University of Yamanashi, Japan
,
Daniel Thalmann
NTU, Singapore & EPFL IC-DO, Switzerland
,
Marina Gavrilova
University of Calgary, Canada

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 June 2017

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National Research Foundation Singapore

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CGI '17

CGI '17: Computer Graphics International 2017

June 27 - 30, 2017

Yokohama, Japan

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Overall Acceptance Rate 35 of 159 submissions, 22%

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Cited By

Angelini MBlasilli GLenti SPalleschi ASantucci G(2022)Effectiveness Error: Measuring and Improving RadViz Visual EffectivenessIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.310487928:12(4770-4786)Online publication date: 1-Dec-2022
https://doi.org/10.1109/TVCG.2021.3104879
Zhang CTan KLi HHong G(2019)A Cost-Sensitive Deep Belief Network for Imbalanced ClassificationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2018.283264830:1(109-122)Online publication date: Jan-2019
https://doi.org/10.1109/TNNLS.2018.2832648
Seyam SAl-Hamed KQureshy ADincer IAgelin-Chaab MRahnamayan S(2019)Multi-objective Optimization of Hydrogen Production in Hybrid Renewable Energy Systems2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8790299(850-857)Online publication date: Jun-2019
https://doi.org/10.1109/CEC.2019.8790299
Wang YLin FSeah H(2018)Evaluation of Star Coordinate BoundariesProceedings of Computer Graphics International 201810.1145/3208159.3208181(87-96)Online publication date: 11-Jun-2018
https://dl.acm.org/doi/10.1145/3208159.3208181
Wang YZhang QLin FGoh CSeah H(2018)PolarViz: a discriminating visualization and visual analytics tool for high-dimensional dataThe Visual Computer10.1007/s00371-018-1558-yOnline publication date: 14-May-2018
https://doi.org/10.1007/s00371-018-1558-y

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