Enhancing grid-density based clustering for high dimensional data

Zhao, Y; Cao, J; Zhang, C; Zhang, S

Enhancing grid-density based clustering for high dimensional data

Zhao, Y

Cao, J Zhang, C

Zhang, S

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Publication Type:: Journal Article
Citation:: Journal of Systems and Software, 2011, 84 (9), pp. 1524 - 1539
Issue Date:: 2011-09-01

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Field	Value	Language
dc.contributor.author	Zhao, Y https://orcid.org/0000-0002-0209-3971	en_US
dc.contributor.author	Cao, J	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.contributor.author	Zhang, S	en_US
dc.date.issued	2011-09-01	en_US
dc.identifier.citation	Journal of Systems and Software, 2011, 84 (9), pp. 1524 - 1539	en_US
dc.identifier.issn	0164-1212	en_US
dc.identifier.uri	http://hdl.handle.net/10453/18227
dc.description.abstract	We propose an enhanced grid-density based approach for clustering high dimensional data. Our technique takes objects (or points) as atomic units in which the size requirement to cells is waived without losing clustering accuracy. For efficiency, a new partitioning is developed to make the number of cells smoothly adjustable; a concept of the ith-order neighbors is defined for avoiding considering the exponential number of neighboring cells; and a novel density compensation is proposed for improving the clustering accuracy and quality. We experimentally evaluate our approach and demonstrate that our algorithm significantly improves the clustering accuracy and quality. © 2011 Elsevier Inc. All rights reserved.	en_US
dc.relation.ispartof	Journal of Systems and Software	en_US
dc.relation.isbasedon	10.1016/j.jss.2011.02.047	en_US
dc.subject.classification	Software Engineering	en_US
dc.title	Enhancing grid-density based clustering for high dimensional data	en_US
dc.type	Journal Article
utslib.citation.volume	9	en_US
utslib.citation.volume	84	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	0804 Data Format	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	9	en_US
pubs.publication-status	Published	en_US
pubs.volume	84	en_US

Abstract:

We propose an enhanced grid-density based approach for clustering high dimensional data. Our technique takes objects (or points) as atomic units in which the size requirement to cells is waived without losing clustering accuracy. For efficiency, a new partitioning is developed to make the number of cells smoothly adjustable; a concept of the ith-order neighbors is defined for avoiding considering the exponential number of neighboring cells; and a novel density compensation is proposed for improving the clustering accuracy and quality. We experimentally evaluate our approach and demonstrate that our algorithm significantly improves the clustering accuracy and quality. © 2011 Elsevier Inc. All rights reserved.

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

http://hdl.handle.net/10453/18227