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Visual exploration of multiway dependencies in multivariate data

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Bei WangAuthors Info & Claims

SA '16: SIGGRAPH ASIA 2016 Symposium on Visualization

Article No.: 2, Pages 1 - 8

https://doi.org/10.1145/3002151.3002162

Published: 28 November 2016 Publication History

Abstract

Analyzing dependencies among variables within multivariate data is an important and challenging problem, especially when the number of data points is large, the number of variables is high, or multiway dependencies are of interest. Several visualization methods have been proposed to aid in the exploration of such information through the direct visualization of the summary statistics. These methods are typically limited to the study of all possible pairwise relationship but in a manner that does not scale to large multidimensional data. In cases where 3-way relationships are investigated, only subsets of dimensions are considered. In this paper, we propose a novel technique for analyzing multiway dependencies through an overview+detail visualization. In this approach, the overview represents all pairwise, 3-, and 4-way dependencies in the data using glyphs that provide a global visual exploration interface for selecting candidate relationships. Exploration is supported through interactive filtering, sorting, zooming, and selection operations. Once selected, the detailed view helps in developing an inference by providing specific information about those selected variables. Various use cases demonstrate how our approach helps to explore multiway dependencies efficiently in large datasets.

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Cited By

Quadri GNieves JWiernik BRosen P(2023)Automatic Scatterplot Design Optimization for Clustering IdentificationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.318988329:10(4312-4327)Online publication date: 1-Oct-2023
https://doi.org/10.1109/TVCG.2022.3189883
Quadri GRosen P(2021)Modeling the Influence of Visual Density on Cluster Perception in Scatterplots Using TopologyIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2020.303036527:2(1829-1839)Online publication date: Feb-2021
https://doi.org/10.1109/TVCG.2020.3030365
Meulemans W(2019)Efficient Optimal Overlap Removal: Algorithms and ExperimentsComputer Graphics Forum10.1111/cgf.1372238:3(713-723)Online publication date: 10-Jul-2019
https://doi.org/10.1111/cgf.13722
Show More Cited By

Index Terms

Visual exploration of multiway dependencies in multivariate data
1. Human-centered computing
  1. Visualization
    1. Visualization techniques

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Published In

cover image ACM Conferences

SA '16: SIGGRAPH ASIA 2016 Symposium on Visualization

November 2016

129 pages

ISBN:9781450345477

DOI:10.1145/3002151

Conference Chairs:
Wei Chen
Zhejiang University, China
,
Daniel Weiskopf
University of Stuttgart, Germany

Copyright © 2016 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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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 November 2016

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National Science Foundation

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SA '16

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SIGGRAPH

SA '16: SIGGRAPH Asia 2016

December 5 - 8, 2016

Macau

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Overall Acceptance Rate 178 of 869 submissions, 20%

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Cited By

Quadri GNieves JWiernik BRosen P(2023)Automatic Scatterplot Design Optimization for Clustering IdentificationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.318988329:10(4312-4327)Online publication date: 1-Oct-2023
https://doi.org/10.1109/TVCG.2022.3189883
Quadri GRosen P(2021)Modeling the Influence of Visual Density on Cluster Perception in Scatterplots Using TopologyIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2020.303036527:2(1829-1839)Online publication date: Feb-2021
https://doi.org/10.1109/TVCG.2020.3030365
Meulemans W(2019)Efficient Optimal Overlap Removal: Algorithms and ExperimentsComputer Graphics Forum10.1111/cgf.1372238:3(713-723)Online publication date: 10-Jul-2019
https://doi.org/10.1111/cgf.13722
Quadri GTuladhar AMalla SRosen P(2017)Visual Analytic Design for Characterizing Air-Sampling Sensor Performance and Operation2017 IEEE Conference on Visual Analytics Science and Technology (VAST)10.1109/VAST.2017.8585678(217-218)Online publication date: Oct-2017
https://doi.org/10.1109/VAST.2017.8585678

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