Malik, M.H., Ghous, H., Ismail, M., Jamshaid, S. and Altaf, J. (2024) Market Basket Analysis for Next Basket Item Prediction Using Data Mining and Machine Learning. Journal of Computing & Biomedical Informatics, in Press.

has been cited by the following article:

• TITLE: Analysis of Changes in Customers’ Market Basket Across Different Branches of a Chain Store Using Association Rules Technique and Its Impact on Product Placement: A Case Study of a Chain Store in Various Areas of Tehran

  AUTHORS: Raheleh Ansari Ashlaghi, Parastoo Mohammadi, Bakhtiar Ostadi

  KEYWORDS: Data Mining, Market Basket Analysis, Association Rules, FP-Growth Algorithm, Tehran

  JOURNAL NAME: Open Access Library Journal, Vol.11 No.9, September 25, 2024

  ABSTRACT: Is the shopping basket composition the same across different branches of a multi-branch store? Is the association between products the same in all chain store branches? In this paper, we studied the market baskets of customers across three branches of a chain store in Tehran using association rule techniques. The product layout in all branches of this chain store is uniform and does not utilize any specific technique. A review of the literature and researchers’ hypotheses reveals that various factors can influence customers’ shopping baskets, including geographical location, season, etc. The main focus of the researchers was on a specific store; therefore, this paper aims to examine the impact of store location on product associations. This study used association rule techniques to examine the relationship between FMCG products, the RFM model for customer clustering, and the FP-Growth algorithm. The examination of customer market baskets in these branches shows that the difference in branches in various regions of Tehran leads to changes in the relationship between purchased products, so multi-branch stores should avoid using a uniform product layout in all branches.