Quantitative Approach on Parallel Coordinates and Scatter Plots for Multidimensional-Data Visual Analytics

Pham, P; Huang, M

Quantitative Approach on Parallel Coordinates and Scatter Plots for Multidimensional-Data Visual Analytics

Pham, P

Huang, M

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Publisher:: Academy Publisher
Publication Type:: Journal Article
Citation:: Journal of computers, 2018, 13 (4), pp. 407 - 416 (10)
Issue Date:: 2018-04-01

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Field	Value	Language
dc.contributor.author	Pham, P https://orcid.org/0000-0003-0954-9959	en_US
dc.contributor.author	Huang, M https://orcid.org/0000-0002-6896-6480	en_US
dc.date.available	2020-05-25T19:21:32Z
dc.date.issued	2018-04-01	en_US
dc.identifier.citation	Journal of computers, 2018, 13 (4), pp. 407 - 416 (10)	en_US
dc.identifier.issn	1796-203X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/106592
dc.description.abstract	Parallel coordinates and scatter plots are two well-known visualization techniques for multidimensional data analytics and often employed cooperatively for flexibility increase in exploration of such data. Existing approaches approximately consider qualitative issues and single attribute comparison, which might face statistic challenges in case of quantitative requirement. This paper introduces a new quantitative approach for visual enhancement of parallel coordinates and scatter plots in term of multiple attribute comparison. The method is based on the visual integration of interactive stacked bars and visual queries on parallel axes and scatter charts. The parallel coordinates play the role of a context view while the scatter charts are for focus details. Using the technique, users could not only quantitatively analyze multivariate data, but also flexibly compare multiple target attributes. Moreover, further investigation is enabled for deep understanding of desired information. The characteristics and usefulness of our approach are demonstrated via a case study with two typical use cases.	en_US
dc.publisher	Academy Publisher	en_US
dc.relation.ispartof	Journal of computers	en_US
dc.relation.isbasedon	10.17706/jcp.13.4.407-416	en_US
dc.title	Quantitative Approach on Parallel Coordinates and Scatter Plots for Multidimensional-Data Visual Analytics	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	13	en_US
utslib.for	0804 Data Format	en_US
utslib.for	08 Information and Computing Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.consider-herdc	true	en_US
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	13	en_US

Abstract:

Parallel coordinates and scatter plots are two well-known visualization techniques for multidimensional data analytics and often employed cooperatively for flexibility increase in exploration of such data. Existing approaches approximately consider qualitative issues and single attribute comparison, which might face statistic challenges in case of quantitative requirement. This paper introduces a new quantitative approach for visual enhancement of parallel coordinates and scatter plots in term of multiple attribute comparison. The method is based on the visual integration of interactive stacked bars and visual queries on parallel axes and scatter charts. The parallel coordinates play the role of a context view while the scatter charts are for focus details. Using the technique, users could not only quantitatively analyze multivariate data, but also flexibly compare multiple target attributes. Moreover, further investigation is enabled for deep understanding of desired information. The characteristics and usefulness of our approach are demonstrated via a case study with two typical use cases.

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http://hdl.handle.net/10453/106592