Market basket analysis in a multiple store environment

Yen Liang Chen, Kwei Tang, Ren Jie Shen, Ya Han Hu

Research output: Contribution to journalArticlepeer-review

104 Scopus citations


Market basket analysis (also known as association-rule mining) is a useful method of discovering customer purchasing patterns by extracting associations or co-occurrences from stores' transactional databases. Because the information obtained from the analysis can be used in forming marketing, sales, service, and operation strategies, it has drawn increased research interest. The existing methods, however, may fail to discover important purchasing patterns in a multi-store environment, because of an implicit assumption that products under consideration are on shelf all the time across all stores. In this paper, we propose a new method to overcome this weakness. Our empirical evaluation shows that the proposed method is computationally efficient, and that it has advantage over the traditional method when stores are diverse in size, product mix changes rapidly over time, and larger numbers of stores and periods are considered.

Original languageEnglish
Pages (from-to)339-354
Number of pages16
JournalDecision Support Systems
Issue number2
StatePublished - Aug 2005


  • Algorithm
  • Association rules
  • Data mining
  • Store chain


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