A Kernel Based Method for Discovering Market Segments in Beef Meat

  • Jorge Díez
  • Juan José del Coz
  • Carlos Sañudo
  • Pere Albertí
  • Antonio Bahamonde
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3721)


In this paper we propose a method for learning the reasons why groups of consumers prefer some food products instead of others. 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.


Preference Function Ranking Function Aging Period Feature Subset Selection Preference Criterion 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jorge Díez
    • 1
  • Juan José del Coz
    • 1
  • Carlos Sañudo
    • 2
  • Pere Albertí
    • 3
  • Antonio Bahamonde
    • 1
  1. 1.Artificial Intelligence CenterUniversity of Oviedo at Gijón (Asturias)Spain
  2. 2.Facultad de VeterinariaUniversity of ZaragozaZaragoza (Aragón)Spain
  3. 3.Service of Agriculture and Food Science ResearchZaragoza (Aragón)Spain

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