零售超市最优产品选择的数据采掘框架--广义PROFSET模型
A Data Mining Framework for Optimal Product Selection in Retail Supermarket Data: The Generalized PROFSET Model
摘要：
近年来，数据采掘的研究人员已经开发出有效的关联规则算法，零售市场购物篮分析。尽管如此，零售商经常抱怨如何采用关联规则来优化具体的零售营销组合决策。正是在这种情况下，在以前的论文中，作者介绍了一种产品选择模型，称为PROFSET。该模型在给定零售商定义的约束条件下，根据产品的交叉销售潜力从产品组合中选择最感兴趣的产品。然而，这种模式遭受了一个重要的缺陷：它不能有效地处理超市的数据，并没有采取任何规定，包括零售类别管理原则。因此，在本文中，作者提出了一个重要的推广现有的模型，以使其适用于超市的数据，以及使零售商添加类别限制的模型。从比利时连锁超市获得的真实世界的数据上的实验产生非常有前途的结果，并证明了广义PROFSET模型的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
