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Repositorio de la Universidad de Oviedo > Producción Bibliográfica de UniOvi: RECOPILA > Capítulos de libros >

Use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10651/31248

Título : A kernel based method for discovering market segments in beef meat
Autor(es) y otros: Díez Peláez, Jorge
Coz Velasco, Juan José del
Sañudo, Carlos
Albertí, P.
Bahamonde Rionda, Antonio
Fecha de publicación : 2005
Editorial : Springer
Versión del editor: http://dx.doi.org/10.1007/11564126_46
Descripción física: p. 462-469
Resumen : In 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
Descripción : 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal
URI : http://hdl.handle.net/10651/31248
ISBN : 978-3-540-29244-9
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