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dc.contributor.authorEstivill-Castro, Vladimiren_US
dc.contributor.editorPhilippe Lencaen_US
dc.date.accessioned2017-04-24T11:31:18Z
dc.date.available2017-04-24T11:31:18Z
dc.date.issued2012en_US
dc.date.modified2013-01-15T00:36:11Z
dc.identifier.issn03029743en_US
dc.identifier.doi10.1007/978-3-642-28320-8_17en_US
dc.identifier.urihttp://hdl.handle.net/10072/45878
dc.description.abstract"The statistical problem of testing cluster validity is essentially unsolved" [5]. We translate the issue of gaining credibility on the output of un-supervised learning algorithms to the supervised learning case. We introduce a notion of instance easiness to supervised learning and link the validity of a clustering to how its output constitutes an easy instance for supervised learning. Our notion of instance easiness for supervised learning extends the notion of stability to perturbations (used earlier for measuring clusterability in the un-supervised setting). We follow the axiomatic and generic formulations for cluster-quality measures. As a result, we inform the trust we can place in a clustering result using standard validity methods for supervised learning, like cross validation.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.format.extent661669 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherSpringeren_US
dc.publisher.placeGermanyen_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofpagefrom197en_US
dc.relation.ispartofpageto208en_US
dc.relation.ispartofjournalLecture Notes in Computer scienceen_US
dc.relation.ispartofvolume7104en_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchPattern Recognition and Data Miningen_US
dc.subject.fieldofresearchcode080109en_US
dc.titleThe Instance Easiness of Supervised Learning for Cluster Validityen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.rights.copyrightCopyright 2011 Springer Berlin / Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.comen_US
gro.date.issued2012
gro.hasfulltextFull Text


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