Abstract
An application of a latent class vector model to preference data is presented. Analysing hedonic ratings, this technique realises simultaneously the clustering of consumers in homogeneous classes on the basis of their preferences and the joint representation of products and classes using a vector model. A probabilistic assumption allows performance of significance tests on the number of clusters and provides a useful tool for interpreting results of preference tests.
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