%0 Journal Article %1 brijs/dmkd/2004 %A Brijs, Tom %A Swinnen, Gilbert %A Vanhoof, Koen %A Wets, Geert %D 2004 %J Data Mining and Knowledge Discovery %K application associationRules dataMining patternMining %N 1 %P 7--23 %T Building an Association Rules Framework to Improve Product Assortment Decisions %U http://dx.doi.org/10.1023/B:DAMI.0000005256.79013.69 %V 8 %X It has been claimed that the discovery of association rules is well suited for applications of market basket analysis to reveal regularities in the purchase behaviour of customers. However today, one disadvantage of associations discovery is that there is no provision for taking into account the business value of an association. Therefore, recent work indicates that the discovery of interesting rules can in fact best be addressed within a microeconomic framework. This study integrates the discovery of frequent itemsets with a (microeconomic) model for product selection (PROFSET). The model enables the integration of both quantitative and qualitative (domain knowledge) criteria. Sales transaction data from a fully automated convenience store are used to demonstrate the effectiveness of the model against a heuristic for product selection based on product-specific profitability. We show that with the use of frequent itemsets we are able to identify the cross-sales potential of product items and use this information for better product selection. Furthermore, we demonstrate that the impact of product assortment decisions on overall assortment profitability can easily be evaluated by means of sensitivity analysis. ER -

@article{brijs/dmkd/2004, abstract = {It has been claimed that the discovery of association rules is well suited for applications of market basket analysis to reveal regularities in the purchase behaviour of customers. However today, one disadvantage of associations discovery is that there is no provision for taking into account the business value of an association. Therefore, recent work indicates that the discovery of interesting rules can in fact best be addressed within a microeconomic framework. This study integrates the discovery of frequent itemsets with a (microeconomic) model for product selection (PROFSET). The model enables the integration of both quantitative and qualitative (domain knowledge) criteria. Sales transaction data from a fully automated convenience store are used to demonstrate the effectiveness of the model against a heuristic for product selection based on product-specific profitability. We show that with the use of frequent itemsets we are able to identify the cross-sales potential of product items and use this information for better product selection. Furthermore, we demonstrate that the impact of product assortment decisions on overall assortment profitability can easily be evaluated by means of sensitivity analysis. ER -}, added-at = {2009-12-03T15:20:59.000+0100}, author = {Brijs, Tom and Swinnen, Gilbert and Vanhoof, Koen and Wets, Geert}, biburl = {https://www.bibsonomy.org/bibtex/2d786431ce7076814a9f14042ce5f4e6a/mboley}, description = {SpringerLink - Zeitschriftenbeitrag}, interhash = {05c76d2af2dead8e1946fcd68b16a99e}, intrahash = {d786431ce7076814a9f14042ce5f4e6a}, journal = {Data Mining and Knowledge Discovery}, keywords = {application associationRules dataMining patternMining}, month = jan, number = 1, pages = {7--23}, timestamp = {2009-12-03T15:20:59.000+0100}, title = {Building an Association Rules Framework to Improve Product Assortment Decisions}, url = {http://dx.doi.org/10.1023/B:DAMI.0000005256.79013.69}, volume = 8, year = 2004 }