Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/17188
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dc.contributor.authorAyed, S-
dc.contributor.authorArzoky, M-
dc.contributor.authorSwift, S-
dc.contributor.authorCounsell, S-
dc.contributor.authorTucker, A-
dc.coverage.spatialLondon-
dc.date.accessioned2018-12-06T11:48:41Z-
dc.date.available2018-09-06-
dc.date.available2018-12-06T11:48:41Z-
dc.date.issued2018-
dc.identifier.citationProceedings of SAI Intelligent Systems Conference, 2018, pp. 1041 - 1055en_US
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/17188-
dc.description.abstractEnsemble and Consensus Clustering address the problem of unifying multiple clustering results into a single output to best reflect the agreement of input methods. They can be used to obtain more stable and robust clustering results in comparison with a single clustering approach. In this study, we propose a novel subset selection method that looks at controlling the number of clustering inputs and datasets in an efficient way. The authors propose a number of manual selection and heuristic search techniques to perform the selection. Our investi‐ gation and experiments demonstrate very promising results. Using these techni‐ ques can ensure better selection methods and datasets for Ensemble and Consensus Clustering and thus more efficient clustering results.en_US
dc.format.extent1041 - 1055-
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.sourceSAI Intelligent Systems Conference-
dc.sourceSAI Intelligent Systems Conference-
dc.subjectEnsemble clusteringen_US
dc.subjectConsensus clusteringen_US
dc.subjectSubset selection problemen_US
dc.subjectHeuristic searchen_US
dc.subjectMachine learningen_US
dc.titleAn Exploratory Study of the Inputs for Ensemble Clustering Technique as a Subset Selection Problemen_US
dc.typeConference Paperen_US
dc.relation.isPartOfProceedings of SAI Intelligent Systems Conference-
pubs.publication-statusPublished-
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