  In recent years, data mining researchers have developed efficient association
rule algorithms for retail market basket analysis. Still, retailers often
complain about how to adopt association rules to optimize concrete retail
marketing-mix decisions. It is in this context that, in a previous paper, the
authors have introduced a product selection model called PROFSET. This model
selects the most interesting products from a product assortment based on their
cross-selling potential given some retailer defined constraints. However this
model suffered from an important deficiency: it could not deal effectively with
supermarket data, and no provisions were taken to include retail category
management principles. Therefore, in this paper, the authors present an
important generalization of the existing model in order to make it suitable for
supermarket data as well, and to enable retailers to add category restrictions
to the model. Experiments on real world data obtained from a Belgian
supermarket chain produce very promising results and demonstrate the
effectiveness of the generalized PROFSET model.
