Market basket analysis in a multiple store environment

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

研究成果: 雜誌貢獻期刊論文同行評審

126 引文 斯高帕斯(Scopus)

摘要

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.

原文???core.languages.en_GB???
頁(從 - 到)339-354
頁數16
期刊Decision Support Systems
40
發行號2
DOIs
出版狀態已出版 - 8月 2005

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