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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)

Abstract

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.

Keywords

Preference Function Ranking Function Aging Period Feature Subset Selection Preference Criterion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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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