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dc.contributor.authorZhao, Hongyaen_US
dc.contributor.authorLiew, Alan Wee-Chungen_US
dc.contributor.authorZ. Wang, Dorisen_US
dc.contributor.authorYan, Hongen_US
dc.date.accessioned2017-05-03T15:20:08Z
dc.date.available2017-05-03T15:20:08Z
dc.date.issued2012en_US
dc.date.modified2013-06-26T03:01:19Z
dc.identifier.issn15748936en_US
dc.identifier.doi10.2174/157489312799304413en_US
dc.identifier.urihttp://hdl.handle.net/10072/47234
dc.description.abstractBiclustering analysis is a useful methodology to discover the local coherent patterns hidden in a data matrix. Unlike the traditional clustering procedure, which searches for groups of coherent patterns using the entire feature set, biclustering performs simultaneous pattern classification in both row and column directions in a data matrix. The technique has found useful applications in many fields but notably in bioinformatics. In this paper, we give an overview of the biclustering problem and review some existing biclustering algorithms in terms of their underlying methodology, search strategy, detected bicluster patterns, and validation strategies. Moreover, we show that geometry of biclustering patterns can be used to solve biclustering problems effectively. Well-known methods in signal and image analysis, such as the Hough transform and relaxation labeling, can be employed to detect the geometrical biclustering patterns. We present performance evaluation results for several of the well known biclustering algorithms, on both artificial and real gene expression datasets. Finally, several interesting applications of biclustering are discussed.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.format.extent883314 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherBentham Scienceen_US
dc.publisher.placeNetherlandsen_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofpagefrom43en_US
dc.relation.ispartofpageto55en_US
dc.relation.ispartofissue1en_US
dc.relation.ispartofjournalCurrent Bioinformaticsen_US
dc.relation.ispartofvolume7en_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchPattern Recognition and Data Miningen_US
dc.subject.fieldofresearchcode080109en_US
dc.titleBiclustering Analysis for Pattern Discovery: Current Techniques, Comparative Studies and Applicationsen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.rights.copyrightCopyright 2012 Bentham Science Publishers. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal website for access to the definitive, published version.en_US
gro.date.issued2012
gro.hasfulltextFull Text


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