A visual method for high-dimensional data cluster exploration

Zhang, KB; Huang, ML; Orgun, MA; Nguyen, QV

A visual method for high-dimensional data cluster exploration

Zhang, KB Huang, ML

Orgun, MA Nguyen, QV

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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), 2009, 5864 LNCS (PART 2), pp. 699 - 709
Issue Date:: 2009-12-01

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Field	Value	Language
dc.contributor.author	Zhang, KB	en_US
dc.contributor.author	Huang, ML https://orcid.org/0000-0002-6896-6480	en_US
dc.contributor.author	Orgun, MA	en_US
dc.contributor.author	Nguyen, QV https://orcid.org/0000-0002-0815-6224	en_US
dc.date.issued	2009-12-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009, 5864 LNCS (PART 2), pp. 699 - 709	en_US
dc.identifier.isbn	364210682X	en_US
dc.identifier.isbn	9783642106828	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10911
dc.description.abstract	Visualization is helpful for clustering high dimensional data. The goals of visualization in data mining are exploration, confirmation and presentation of the clustering results. However, the most of visual techniques developed for cluster analysis are primarily focused on cluster presentation rather than cluster exploration. Several techniques have been proposed to explore cluster information by visualization, but most of them depend heavily on the individual user's experience. Inevitably, this incurs subjectivity and randomness in the clustering process. In this paper, we employ the statistical features of datasets as predictions to estimate the number of clusters by a visual technique called HOV3. This approach mitigates the problem of the randomness and subjectivity of the user during the process of cluster exploration by other visual techniques. As a result, our approach provides an effective visual method for cluster exploration. © 2009 Springer-Verlag Berlin Heidelberg.	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	10.1007/978-3-642-10684-2_78	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A visual method for high-dimensional data cluster exploration	en_US
dc.type	Conference Proceeding
utslib.citation.volume	PART 2	en_US
utslib.citation.volume	5864 LNCS	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Bangkok, Thailand	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
utslib.copyright.status	open_access
pubs.issue	PART 2	en_US
pubs.publication-status	Published	en_US
pubs.volume	5864 LNCS	en_US

Abstract:

Visualization is helpful for clustering high dimensional data. The goals of visualization in data mining are exploration, confirmation and presentation of the clustering results. However, the most of visual techniques developed for cluster analysis are primarily focused on cluster presentation rather than cluster exploration. Several techniques have been proposed to explore cluster information by visualization, but most of them depend heavily on the individual user's experience. Inevitably, this incurs subjectivity and randomness in the clustering process. In this paper, we employ the statistical features of datasets as predictions to estimate the number of clusters by a visual technique called HOV3. This approach mitigates the problem of the randomness and subjectivity of the user during the process of cluster exploration by other visual techniques. As a result, our approach provides an effective visual method for cluster exploration. © 2009 Springer-Verlag Berlin Heidelberg.

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http://hdl.handle.net/10453/10911