Biclustering based on association analysis

dc.contributor.authorΒουτσινάς, Βασίλης
dc.contributor.otherBoutsinas, Basilis
dc.date.accessioned2013-03-27T09:16:18Z
dc.date.available2013-03-27T09:16:18Z
dc.date.copyright2013-03-26
dc.date.issued2013-03-27
dc.description.abstractClustering has been applied in a wide variety of disciplines and has also been utilized in many scientific areas. Usually, clustering algorithms construct either clusters of rows or clusters of columns of the input data matrix. Biclustering is a methodology where biclusters are formed by both a subset of rows and a subset of columns, such that objects represented by the first are the most similar to each other when compared over the latter. In this paper, we introduce a new biclustering technique, based on association rule mining, which can support different well-known biclustering models proposed in the literature. Experimental tests demonstrate the accuracy and efficiency of the proposed technique with respect to well known related ones.el
dc.description.translatedabstract-el
dc.identifier.urihttp://hdl.handle.net/10889/5919
dc.language.isoenel
dc.subjectBiclusteringel
dc.subjectClusteringel
dc.subjectAssociation rule miningel
dc.subjectData miningel
dc.subjectHigh dimensionalityel
dc.subject.alternativeΟμαδοποίησηel
dc.subject.alternativeΕξόρυξη δεδομένωνel
dc.titleBiclustering based on association analysisel
dc.typeTechnical Reportel
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