Parallel rough set: Dimensionality reduction and feature discovery of multi-dimensional data in visualization

Huang, TH; Huang, ML; Jin, JS

Parallel rough set: Dimensionality reduction and feature discovery of multi-dimensional data in visualization

Huang, TH Huang, ML

Jin, JS

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, 7063 LNCS (PART 2), pp. 99 - 108
Issue Date:: 2011-11-28

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Field	Value	Language
dc.contributor.author	Huang, TH	en_US
dc.contributor.author	Huang, ML https://orcid.org/0000-0002-6896-6480	en_US
dc.contributor.author	Jin, JS	en_US
dc.date.issued	2011-11-28	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, 7063 LNCS (PART 2), pp. 99 - 108	en_US
dc.identifier.isbn	9783642249570	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31310
dc.description.abstract	Attempt to visualize high dimensional datasets typically encounter over plotting and decline in visual comprehension that makes the knowledge discovery and feature subset analysis difficult. Hence, reshaping the datasets using dimensionality reduction technique is paramount by removing the superfluous attributes to improve visual analytics. In this work, we applied rough set theory as dimensionality reduction and feature selection methods on visualization to facilitate knowledge discovery of multi-dimensional datasets. We provided the case study using real datasets and comparison against other methods to demonstrate the effectiveness of our approach. © 2011 Springer-Verlag.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-24958-7_12	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Parallel rough set: Dimensionality reduction and feature discovery of multi-dimensional data in visualization	en_US
dc.type	Conference Proceeding
utslib.citation.volume	PART 2	en_US
utslib.citation.volume	7063 LNCS	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Shanghai, China
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
utslib.copyright.status	closed_access
pubs.issue	PART 2	en_US
pubs.publication-status	Published	en_US
pubs.volume	7063 LNCS	en_US

Abstract:

Attempt to visualize high dimensional datasets typically encounter over plotting and decline in visual comprehension that makes the knowledge discovery and feature subset analysis difficult. Hence, reshaping the datasets using dimensionality reduction technique is paramount by removing the superfluous attributes to improve visual analytics. In this work, we applied rough set theory as dimensionality reduction and feature selection methods on visualization to facilitate knowledge discovery of multi-dimensional datasets. We provided the case study using real datasets and comparison against other methods to demonstrate the effectiveness of our approach. © 2011 Springer-Verlag.

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http://hdl.handle.net/10453/31310