Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/10529
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dc.contributor.authorde Sa,CRen
dc.contributor.authorKnobbe,Aen
dc.contributor.authorCarlos Manuel Soaresen
dc.contributor.authorAlípio Jorgeen
dc.contributor.authorPaulo Jorge Azevedoen
dc.contributor.authorDuivesteijn,Wen
dc.contributor.other4981en
dc.contributor.other5606en
dc.contributor.other5001en
dc.date.accessioned2019-12-16T11:17:54Z-
dc.date.available2019-12-16T11:17:54Z-
dc.date.issued2018en
dc.identifier.urihttp://repositorio.inesctec.pt/handle/123456789/10529-
dc.identifier.urihttp://dx.doi.org/10.1007/s10994-018-5743-zen
dc.description.abstractExceptional preferences mining (EPM) is a crossover between two subfields of data mining: local pattern mining and preference learning. EPM can be seen as a local pattern mining task that finds subsets of observations where some preference relations between labels significantly deviate from the norm. It is a variant of subgroup discovery, with rankings of labels as the target concept. We employ several quality measures that highlight subgroups featuring exceptional preferences, where the focus of what constitutes ‘exceptional’ varies with the quality measure: two measures look for exceptional overall ranking behavior, one measure indicates whether a particular label stands out from the rest, and a fourth measure highlights subgroups with unusual pairwise label ranking behavior. We explore a few datasets and compare with existing techniques. The results confirm that the new task EPM can deliver interesting knowledge. © 2018 The Author(s)en
dc.languageengen
dc.titleDiscovering a taste for the unusual: exceptional models for preference miningen
dc.typePublicationen
dc.typearticleen
Appears in Collections:CESE - Indexed Articles in Journals

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