A data mining approach to product assortment and shelf space allocation

Mu-Chen Chen*, Chia Ping Lin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

39 Scopus citations


In retailing, a variety of products compete to be displayed in the limited shelf space since it has a significant effect on demands. To affect customers' purchasing decisions, retailers properly make decisions about which products to display (product assortment) and how much shelf space to allocate the stocked products (shelf space allocation). In the previous studies, researchers usually employed the space elasticity to optimize product assortment and space allocation models. The space elasticity is usually used to construct the relationship between shelf space and product demand. However, the large number of parameters requiring to estimate and the he non-linear nature of space elasticity can reduce the efficacy of the space elasticity based models. This paper utilizes a popular data mining approach, association rule mining, instead of space elasticity to resolve the product assortment and allocation problems in retailing. In this paper, the multi-level association rule mining is applied to explore the relationships between products as well as between product categories. Because association rules are obtained by directly analyzing the transaction database, they can generate more reliable information to shelf space management.

Original languageEnglish
Pages (from-to)976-986
Number of pages11
JournalExpert Systems with Applications
Issue number4
StatePublished - 1 May 2007


  • Data mining
  • Multi-level association rules
  • Shelf space management
  • Zero-one integer programming

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