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Article: A semi-supervised approach to projected clustering with applications to microarray data.

TitleA semi-supervised approach to projected clustering with applications to microarray data.
Authors
Issue Date2009
PublisherInderscience Publishers. The Journal's web site is located at http://www.inderscience.com/ijdmb
Citation
International Journal Of Data Mining And Bioinformatics, 2009, v. 3 n. 3, p. 229-259 How to Cite?
AbstractRecent studies have suggested that extremely low dimensional projected clusters exist in real datasets. Here, we propose a new algorithm for identifying them. It combines object clustering and dimension selection, and allows the input of domain knowledge in guiding the clustering process. Theoretical and experimental results show that even a small amount of input knowledge could already help detect clusters with only 1% of the relevant dimensions. We also show that this semi-supervised algorithm can perform knowledge-guided selective clustering when there are multiple meaningful object groupings. The algorithm is also shown effective in analysing a microarray dataset.
Persistent Identifierhttp://hdl.handle.net/10722/152413
ISSN
2015 Impact Factor: 0.528
2015 SCImago Journal Rankings: 0.289
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYip, KYen_US
dc.contributor.authorCheung, Len_US
dc.contributor.authorCheung, DWen_US
dc.contributor.authorJing, Len_US
dc.contributor.authorNg, MKen_US
dc.date.accessioned2012-06-26T06:38:15Z-
dc.date.available2012-06-26T06:38:15Z-
dc.date.issued2009en_US
dc.identifier.citationInternational Journal Of Data Mining And Bioinformatics, 2009, v. 3 n. 3, p. 229-259en_US
dc.identifier.issn1748-5673en_US
dc.identifier.urihttp://hdl.handle.net/10722/152413-
dc.description.abstractRecent studies have suggested that extremely low dimensional projected clusters exist in real datasets. Here, we propose a new algorithm for identifying them. It combines object clustering and dimension selection, and allows the input of domain knowledge in guiding the clustering process. Theoretical and experimental results show that even a small amount of input knowledge could already help detect clusters with only 1% of the relevant dimensions. We also show that this semi-supervised algorithm can perform knowledge-guided selective clustering when there are multiple meaningful object groupings. The algorithm is also shown effective in analysing a microarray dataset.en_US
dc.languageengen_US
dc.publisherInderscience Publishers. The Journal's web site is located at http://www.inderscience.com/ijdmben_US
dc.relation.ispartofInternational journal of data mining and bioinformaticsen_US
dc.rightsInternational Journal of Data Mining and Bioinformatics. Copyright © Inderscience Publishers.-
dc.subject.meshAlgorithmsen_US
dc.subject.meshArtificial Intelligenceen_US
dc.subject.meshCluster Analysisen_US
dc.subject.meshHumansen_US
dc.subject.meshNeoplasm Proteins - Metabolismen_US
dc.subject.meshOligonucleotide Array Sequence Analysis - Methodsen_US
dc.subject.meshPattern Recognition, Automateden_US
dc.subject.meshSoftwareen_US
dc.titleA semi-supervised approach to projected clustering with applications to microarray data.en_US
dc.typeArticleen_US
dc.identifier.emailCheung, DW:dcheung@cs.hku.hken_US
dc.identifier.authorityCheung, DW=rp00101en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1504/IJDMB.2009.026700en_US
dc.identifier.pmid19623769-
dc.identifier.scopuseid_2-s2.0-68049118580en_US
dc.identifier.hkuros164469-
dc.identifier.volume3en_US
dc.identifier.issue3en_US
dc.identifier.spage229en_US
dc.identifier.epage259en_US
dc.identifier.isiWOS:000267790300001-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridYip, KY=7101909946en_US
dc.identifier.scopusauthoridCheung, L=36840358100en_US
dc.identifier.scopusauthoridCheung, DW=34567902600en_US
dc.identifier.scopusauthoridJing, L=8893085600en_US
dc.identifier.scopusauthoridNg, MK=7202076432en_US

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