Clutter reduction in multi-dimensional visualization of incomplete data using sugiyama algorithm

Lu, LF; Huang, ML; Chen, YW; Liang, J; Nguyen, QV

Clutter reduction in multi-dimensional visualization of incomplete data using sugiyama algorithm

Lu, LF Huang, ML

Chen, YW Liang, J

Nguyen, QV

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Conference on Information Visualisation, 2012, pp. 93 - 99
Issue Date:: 2012-10-31

Closed Access

	Filename	Description	Size
	2012002777OK.pdf		1.7 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Lu, LF	en_US
dc.contributor.author	Huang, ML https://orcid.org/0000-0002-6896-6480	en_US
dc.contributor.author	Chen, YW	en_US
dc.contributor.author	Liang, J https://orcid.org/0000-0001-7179-5208	en_US
dc.contributor.author	Nguyen, QV	en_US
dc.date.issued	2012-10-31	en_US
dc.identifier.citation	Proceedings of the International Conference on Information Visualisation, 2012, pp. 93 - 99	en_US
dc.identifier.isbn	9780769547718	en_US
dc.identifier.issn	1093-9547	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22923
dc.description.abstract	Visualization of uncertainty in datasets is a new field of research, which aims to represent incomplete data for analysis in real scenarios. In many cases, datasets, especially multi-dimensional datasets, often contain either errors or uncertain values. To address this challenge, we may treat these uncertainties as scalar values like probability. For visual representation in parallel coordinates, we draw a small "circle" to temporarily define a dummy vertex for an uncertain value of a data item, at the crossing point between polylines and the axis of certain dimension. Furthermore, these temporary positions of uncertainty could be permuted to achieve visual effectiveness. This feature provides a great opportunity by optimizing the order of uncertain values to tackle another important challenge in information visualization: clutter reduction. Visual clutter always obscures the visualizing structure even in small datasets. In this paper, we apply Sugiyama's layered directed graph drawing algorithm into parallel coordinates visualization to minimize the number of edge crossing among polylines, which has significantly improved the readability of visual structure. Experiments in case studies have shown the effectiveness of our new methods for clutter reduction in parallel coordinates visualization. These experiments also imply that besides visual clutter, the number of uncertain values and the type of multi-dimensional data are important attributes that affect visualization performance in this field. © 2012 IEEE.	en_US
dc.relation.ispartof	Proceedings of the International Conference on Information Visualisation	en_US
dc.relation.isbasedon	10.1109/IV.2012.26	en_US
dc.title	Clutter reduction in multi-dimensional visualization of incomplete data using sugiyama algorithm	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Montpellier, France	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	closed_access
pubs.publication-status	Published	en_US

Abstract:

Visualization of uncertainty in datasets is a new field of research, which aims to represent incomplete data for analysis in real scenarios. In many cases, datasets, especially multi-dimensional datasets, often contain either errors or uncertain values. To address this challenge, we may treat these uncertainties as scalar values like probability. For visual representation in parallel coordinates, we draw a small "circle" to temporarily define a dummy vertex for an uncertain value of a data item, at the crossing point between polylines and the axis of certain dimension. Furthermore, these temporary positions of uncertainty could be permuted to achieve visual effectiveness. This feature provides a great opportunity by optimizing the order of uncertain values to tackle another important challenge in information visualization: clutter reduction. Visual clutter always obscures the visualizing structure even in small datasets. In this paper, we apply Sugiyama's layered directed graph drawing algorithm into parallel coordinates visualization to minimize the number of edge crossing among polylines, which has significantly improved the readability of visual structure. Experiments in case studies have shown the effectiveness of our new methods for clutter reduction in parallel coordinates visualization. These experiments also imply that besides visual clutter, the number of uncertain values and the type of multi-dimensional data are important attributes that affect visualization performance in this field. © 2012 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/22923