document: A microeconomic data mining problem : customer-oriented catalog segmentation The microeconomic framework for data mining ( 7 ) assumes that an enterprise chooses a decision maximizing the overall utility over all customers where the contribution of a customer is a function of the data available on that customer . In Catalog Segmentation , the enterprise wants to design k product catalogs of size r that maximize the overall number of catalog products purchased . However , there are many applications where a customer , once attracted to an enterprise , would purchase more products beyond the ones contained in the catalog . Therefore , in this paper , we investigate an alternative problem formulation , that we call Customer-Oriented Catalog Segmentation , where the overall utility is measured by the number of customers that have at least a specified minimum interest t in the catalogs . We formally introduce the Customer-Oriented Catalog Segmentation problem and discuss its complexity . Then we investigate two different paradigms to design efficient , approximate algorithms for the Customer-Oriented Catalog Segmentation problem , greedy ( deterministic ) and randomized algorithms . Since greedy algorithms may be trapped in a local optimum and randomized algorithms crucially depend on a reasonable initial solution , we explore a combination of these two paradigms . Our experimental evaluation on synthetic and real data demonstrates that the new algorithms yield catalogs of significantly higher utility compared to classical Catalog Segmentation algorithms .
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