Research of clustering algorithm based on different data field model

Meng, HD; Wu, PF; Song, YC; Xu, GD

Research of clustering algorithm based on different data field model

Meng, HD Wu, PF Song, YC Xu, GD

Permalink

Publication Type:: Journal Article
Citation:: Advanced Materials Research, 2013, 760-762 pp. 1925 - 1929
Issue Date:: 2013-10-31

Closed Access

	Filename	Description	Size
	re.pdf	Published Version	603.54 kB	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	Meng, HD	en_US
dc.contributor.author	Wu, PF	en_US
dc.contributor.author	Song, YC	en_US
dc.contributor.author	Xu, GD https://orcid.org/0000-0003-4493-6663	en_US
dc.date.issued	2013-10-31	en_US
dc.identifier.citation	Advanced Materials Research, 2013, 760-762 pp. 1925 - 1929	en_US
dc.identifier.isbn	9783037857731	en_US
dc.identifier.issn	1022-6680	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121437
dc.description.abstract	Data field clustering algorithm possesses dynamic characteristics compared with other clustering algorithms. By changing the parameters of the data field model, the results can be dynamically adjusted to meet the target of feature extraction and knowledge discovery in different scales, but the selection and construction of data field model can give rise to different clustering results. This paper presents the different effectiveness of clustering based on various of data field models and its parameters, provides with the scheme to chose the best data field model fitting to the characteristics of the data radiation, and verifies that the best clustering effectiveness can be achieved with the value of radial energy in the golden section. © (2013) Trans Tech Publications, Switzerland.	en_US
dc.relation.ispartof	Advanced Materials Research	en_US
dc.relation.isbasedon	10.4028/www.scientific.net/AMR.760-762.1925	en_US
dc.title	Research of clustering algorithm based on different data field model	en_US
dc.type	Journal Article
utslib.citation.volume	760-762	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	09 Engineering	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 - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	760-762	en_US

Abstract:

Data field clustering algorithm possesses dynamic characteristics compared with other clustering algorithms. By changing the parameters of the data field model, the results can be dynamically adjusted to meet the target of feature extraction and knowledge discovery in different scales, but the selection and construction of data field model can give rise to different clustering results. This paper presents the different effectiveness of clustering based on various of data field models and its parameters, provides with the scheme to chose the best data field model fitting to the characteristics of the data radiation, and verifies that the best clustering effectiveness can be achieved with the value of radial energy in the golden section. © (2013) Trans Tech Publications, Switzerland.

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

http://hdl.handle.net/10453/121437