A new axes Re-ordering method in parallel coordinates visualization

Lu, LF; Huang, ML; Huang, TH

A new axes Re-ordering method in parallel coordinates visualization

Lu, LF Huang, ML

Huang, TH

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012, 2012, 2 pp. 252 - 257
Issue Date:: 2012-12-01

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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	Huang, TH	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012, 2012, 2 pp. 252 - 257	en_US
dc.identifier.isbn	9780769549132	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22920
dc.description.abstract	Visualization and interaction of multidimensional data always requires optimized solutions to integrate the display, exploration and analytical reasoning of data into one visual pipeline for human-centered data analysis and interpretation. Parallel coordinate, as one of the popular multidimensional data visualization techniques, is suffered from the visual clutter problem. Though changing the ordering of axis is a straightforward way to address it, optimizing the order of axis is a NP-complete problem. In this paper, we propose a new axes re-ordering method in parallel coordinates visualization: a similarity-based method, which is based on the combination of Nonlinear Correlation Coefficient (NCC) and Singular Value Decomposition (SVD) algorithms. By using this approach, the first remarkable axe can be selected based on mathematics theory and all axis are re-ordered in line with the degree of similarities among them. Meanwhile, we would also propose a measurement of contribution rate of each dimension to reveal the property hidden in the dataset. At last, case studies demonstrate the rationale and effectiveness of our approaches: NCC reordering method can enlarge the mean crossing angles and reduce the amount of polylines between the neighboring axes. It can reduce the computational complexity greatly in comparison with other re-ordering methods. © 2012 IEEE.	en_US
dc.relation.ispartof	Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012	en_US
dc.relation.isbasedon	10.1109/ICMLA.2012.148	en_US
dc.title	A new axes Re-ordering method in parallel coordinates visualization	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Boca Raton, FL, USA	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
pubs.volume	2	en_US

Abstract:

Visualization and interaction of multidimensional data always requires optimized solutions to integrate the display, exploration and analytical reasoning of data into one visual pipeline for human-centered data analysis and interpretation. Parallel coordinate, as one of the popular multidimensional data visualization techniques, is suffered from the visual clutter problem. Though changing the ordering of axis is a straightforward way to address it, optimizing the order of axis is a NP-complete problem. In this paper, we propose a new axes re-ordering method in parallel coordinates visualization: a similarity-based method, which is based on the combination of Nonlinear Correlation Coefficient (NCC) and Singular Value Decomposition (SVD) algorithms. By using this approach, the first remarkable axe can be selected based on mathematics theory and all axis are re-ordered in line with the degree of similarities among them. Meanwhile, we would also propose a measurement of contribution rate of each dimension to reveal the property hidden in the dataset. At last, case studies demonstrate the rationale and effectiveness of our approaches: NCC reordering method can enlarge the mean crossing angles and reduce the amount of polylines between the neighboring axes. It can reduce the computational complexity greatly in comparison with other re-ordering methods. © 2012 IEEE.

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http://hdl.handle.net/10453/22920