Retailing is an industry with high level of competition. It is a customer-based industry which depends on how it could be aware of what the customers’ needs and requirements are. One technique most used in supermarkets is the mix merchandise. The purpose of this paper is to identify associated products, which then grouped in mix merchandise with the use of market basket analysis. This association between products then will be applied in the design layout of the product in the supermarket. The process of identifying the related products bought together in one transaction is done by using data mining technique. Apriori algorithm is chosen as a method in the data mining process. Using WEKA (Waikato Environment for Knowledge Analysis) software, the association rule between products is calculated. The results found five category association rules and fourteen sub-category association rules. These associations then will be interpreted as confidence and support to become consideration for the product layout.


R. Agrawal, T. Imielinski, A. Swami, Proceeding of the ACM SIGMOD Conference on Management of Data, 1993.

R. Agrawal, R. Srikant. The International Conference on Very Large Databases, 1994.

I. H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java

Implementations, chapter 8, http://www.cs.waikato.ac.nz, 2000.

A. Roberts, Guide to WEKA, http://www.comp.leeds.ac.uk/andyr, 2005.

M. Levy, B. Weitz, Retailing Management, McGraw-Hill, New York, 2001.



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.