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A kernel based method for discovering market segments in beef meat

dc.contributor.authorDíez Peláez, Jorge 
dc.contributor.authorCoz Velasco, Juan José del 
dc.contributor.authorSañudo, Carlos
dc.contributor.authorAlbertí, P.
dc.contributor.authorBahamonde Rionda, Antonio 
dc.date.accessioned2015-06-16T10:05:22Z
dc.date.available2015-06-16T10:05:22Z
dc.date.issued2005
dc.identifier.isbn978-3-540-29244-9
dc.identifier.urihttp://hdl.handle.net/10651/31248
dc.description9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugalspa
dc.description.abstractIn this paper we propose a method for learning the reasons why groups of consumers prefer some food products instead of others of the same type. We emphasize the role of groups given that, from a practical point of view, they may represent market segments that demand different products. Our method starts representing people’s preferences in a metric space; there we are able to define a kernel based similarity function that allows a clustering algorithm to discover significant groups of consumers with homogeneous tastes. Finally in each cluster, we learn, with a SVM, a function that explains the tastes of the consumers grouped in the cluster. To illustrate our method, a real case of consumers of beef meat was studied. The panel was formed by 171 people who rated 303 samples of meat from 101 animals with 3 different aging periods
dc.format.extentp. 462-469spa
dc.language.isoengspa
dc.publisherSpringerspa
dc.relation.ispartofKnowledge Discovery in Databases: PKDD 2005spa
dc.rights© 2005 Springer
dc.titleA kernel based method for discovering market segments in beef meateng
dc.typebook partspa
dc.identifier.doi10.1007/11564126_46
dc.relation.publisherversionhttp://dx.doi.org/10.1007/11564126_46spa
dc.rights.accessRightsopen access


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