2007/02/28 by Letrémy, Patrick, Eric Esposito, Cottrell, Marie +5
Business, Management and Accounting · Computer Science · #Advanced Clustering Algorithms Research #Customer churn and segmentation #Data Mining Algorithms and Applications #FOS: Computer and information sciences #FOS: Mathematics #Neural and Evolutionary Computing (cs.NE) #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.math/0702866
openalex publication_date 2007/02/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.