University of Aberdeen logo

AURA - Aberdeen University Research Archive

 

Application of feature selection methods for automated clustering analysis : a review on synthetic datasets

Simple item page
dc.contributor.authorAhmad, Aliyu Usman
dc.contributor.authorStarkey, Andrew
dc.contributor.institutionUniversity of Aberdeen.Engineeringen
dc.contributor.institutionUniversity of Aberdeen.Engineeringen
dc.contributor.institutionUniversity of Aberdeen.Energyen
dc.contributor.institutionUniversity of Aberdeen.Centre for Applied Dynamics Research (CADR)en
dc.date.accessioned2017-04-25T12:28:18Z
dc.date.available2017-04-25T12:28:18Z
dc.date.issued2018-04
dc.descriptionOpen via Springer Compact Agreementen
dc.description.statusPeer revieweden
dc.format.extent12
dc.format.extent1800147
dc.identifier97302513
dc.identifier358ab5b9-b1d2-46c2-bb92-c6ca292e7240
dc.identifier85018844153
dc.identifier.citationAhmad, A U & Starkey, A 2018, 'Application of feature selection methods for automated clustering analysis : a review on synthetic datasets', Neural Computing and Applications, vol. 29, no. 7, pp. 317-328. https://doi.org/10.1007/s00521-017-3005-9en
dc.identifier.doi10.1007/s00521-017-3005-9
dc.identifier.iss7en
dc.identifier.issn0941-0643
dc.identifier.urihttp://hdl.handle.net/2164/8544
dc.identifier.vol29en
dc.language.isoeng
dc.relation.ispartofNeural Computing and Applicationsen
dc.subjectclusteringen
dc.subjectself-organising neural network mapen
dc.subjectfeature selectionen
dc.subjectautomationen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectGeneral Computer Scienceen
dc.subject.lccQA75en
dc.titleApplication of feature selection methods for automated clustering analysis : a review on synthetic datasetsen
dc.typeJournal articleen

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
Application_of_feature_selection_methods_for_automated_clustering_analysis.pdf
Size:
1.72 MB
Format:
Adobe Portable Document Format
Download

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
8.29 KB
Format:
Plain Text
Description:
Download

Collections

All research
Engineering research
University Office research