A data mining framework for optimal product selection in retail supermarket data: the generalized PROFSET model
A data mining framework for optimal product selection in retail supermarket data: the generalized PROFSET model
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DOI：
10.1145/347090.347156
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发表时间：
2000-08
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影响因子：
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通讯作者：
T. Brijs;Bart Goethals;G. Swinnen;K. Vanhoof;G. Wets
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文献类型：
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作者：
T. Brijs;Bart Goethals;G. Swinnen;K. Vanhoof;G. Wets
近年来，数据挖掘研究人员开发了高效的关联规则算法用于零售购物篮分析。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。正是在这种背景下，在之前的一篇论文中，作者介绍了一个名为PROFSET的产品选择模型。在给定零售商定义的约束条件下，该模型根据产品的交叉销售潜力从产品分类中选择最有趣的产品。然而，这个模型有一个重要的缺陷：它不能有效地处理超市数据，并且没有规定包括零售品类管理原则。因此，在本文中，作者对现有模型进行了重要的推广，以使其也适用于超市数据，并使零售商能够在模型中添加类别限制。从比利时一家连锁超市获得的真实世界数据的实验产生了非常有希望的结果，并证明了广义PROFSET模型的有效性。
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.