research-article

Deep Exploration of Multidimensional Data with Linkable Scatterplots

Authors:

Quang Vinh Nguyen,

Mao Lin HuangAuthors Info & Claims

VINCI '16: Proceedings of the 9th International Symposium on Visual Information Communication and Interaction

Pages 43 - 50

https://doi.org/10.1145/2968220.2968248

Published: 24 September 2016 Publication History

Abstract

Clarity, simplicity and visual adjustability to the preference of the analyst are key aspects of the visualization techniques required by visual analytics in broad sense. Scatterplots and scatterplot matrices are commonly used for visually analyzing multidimensional multivariate data. This paper presents a new approach for deep visual exploration of large multi-attribute data using linkable scatterplots. Proposed method overcomes the limitations of the single scatterplot by providing more plot panels for better comparison while it reduces the unnecessary number of panels of the scatterplot matrix method. The panels are fully interactive and linking together where variables can be mapped on axes independently or on common visual attributes such as color, size and shape. We illustrate the effectiveness of proposed linkable scatterplot method on various data sets.

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Nguyen QHuang MSimoff S(2022)Using Hybrid Scatterplots for Visualizing Multi-dimensional DataIntegrating Artificial Intelligence and Visualization for Visual Knowledge Discovery10.1007/978-3-030-93119-3_20(517-538)Online publication date: 5-Jun-2022
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VINCI '16: Proceedings of the 9th International Symposium on Visual Information Communication and Interaction

September 2016

173 pages

ISBN:9781450341493

DOI:10.1145/2968220

General Chair:
Kang Zhang,
Program Chair:
Andreas Kerren

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 ACM 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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Published: 24 September 2016

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VINCI '16

VINCI '16: The 9th International Symposium on Visual Information Communication and Interaction

September 24 - 26, 2016

TX, Dallas, USA

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VINCI '16 Paper Acceptance Rate 14 of 42 submissions, 33%;

Overall Acceptance Rate 71 of 193 submissions, 37%

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Cited By

Taka EStein SWilliamson J(2024)Does Interactive Conditioning Help Users Better Understand the Structure of Probabilistic Models?IEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.323196730:7(3256-3267)Online publication date: Jul-2024
https://doi.org/10.1109/TVCG.2022.3231967
Nguyen QHuang MSimoff S(2022)Using Hybrid Scatterplots for Visualizing Multi-dimensional DataIntegrating Artificial Intelligence and Visualization for Visual Knowledge Discovery10.1007/978-3-030-93119-3_20(517-538)Online publication date: 5-Jun-2022
https://doi.org/10.1007/978-3-030-93119-3_20
Lee JCoelho DMueller K(2021)Cluster Appearance Glyphs: A Methodology for Illustrating High-Dimensional Data Patterns in 2-D Data LayoutsInformation10.3390/info1301000313:1(3)Online publication date: 23-Dec-2021
https://doi.org/10.3390/info13010003
Tegegne YQu ZQian YNguyen Q(2021)Parallel Nonlinear Dimensionality Reduction Using GPU AccelerationData Mining10.1007/978-981-16-8531-6_1(3-15)Online publication date: 9-Dec-2021
https://doi.org/10.1007/978-981-16-8531-6_1
Perkhofer LHofer P(2021)Optimizing Scatterplot-Matrices for Decision-Support:Information Systems and Neuroscience10.1007/978-3-030-88900-5_8(63-76)Online publication date: 29-Oct-2021
https://doi.org/10.1007/978-3-030-88900-5_8
Nguyen QLin Huang MSimoff S(2020)Enhancing Scatter-plots with Start-plots for Visualising Multi-dimensional Data2020 24th International Conference Information Visualisation (IV)10.1109/IV51561.2020.00023(80-85)Online publication date: Sep-2020
https://doi.org/10.1109/IV51561.2020.00023
Nguyen QMiller NArness DHuang WHuang MSimoff S(2020)Evaluation on interactive visualization data with scatterplotsVisual Informatics10.1016/j.visinf.2020.09.004Online publication date: Sep-2020
https://doi.org/10.1016/j.visinf.2020.09.004
Nguyen QEngelke UBednarz T(2017)Big data visual analyticsSIGGRAPH Asia 2017 Courses10.1145/3134472.3134516(1-203)Online publication date: 27-Nov-2017
https://dl.acm.org/doi/10.1145/3134472.3134516

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