Clustering Discrete Choice Data
Donatella Vicari, Marco Alfò · 2010
When clustering discrete choice (e.g. customers by products) data, we may be interested in partitioning individuals in disjoint classes which are homogeneous with respect to product choices and, given the availability of individual- or outcome-specific covariates, in investigating on how these affect the likelihood to be in certain categories (i.e. to choose certain products). Here, a model for joint clustering of statistical units (e.g. consumers) and variables (e.g. products) is proposed in a mixture modeling framework, and the corresponding (modified) EM algorithm is sketched. The proposed model can be easily linked to similar proposals appeared in various contexts, such as in co-clustering gene expression data or in clustering words and documents in webmining data analysis.