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Proceedings of the 9th International Work-Conference on Artificial Neural Networks, IWANN 2007, San Sebastián, Spain, June 20-22, 2007.; We propose an easy-to-use methodology to allocate one of the groups which have been previously built from a complete learning data base, to new individuals. The learning data base contains continuous and categorical variables for each individual. The groups (clusters) are built by using only the continuous variables and described with the help of the categorical ones. For the new individuals, only the categorical variables are available, and it is necessary to define a model which computes the probabilities to belong to each of the clusters, by using only the categorical variables. Then this model provides a decision rule to assign the new individuals and gives an efficient tool to decision-makers. This tool is shown to be very efficient for customers allocation in consumer clusters for marketing purposes, for example.

Conference WSOM 05 5-8 September 2005; International audience; The paper presents a method to allocate a class of a Kohonen map to a new customer without knowing anything about the variables used for the classification. In this study, a classification of daily gas load profiles is performed on a panel of Gaz de France customers. Then, we use a multinomial logit model to allocate a class to a new customer according to its additional variables. With this model, it is also possible to infer the probability to belong to each of the Kohonen classes. The main application for Gaz de France is the inference of the daily gas load for any customer.