Association Mining Rule Applied to Optimal Shopping Path and Layout: A Case Study of Supermarket in Thailand

Abstract

The association rule was a data mining techniques, which was important in knowledge discovery association of data. Finding the association between each shelf could benefit both customers and business how to place layout in supermarket. This work was focused on exploring the association rules analysis applied to each shelve of products by comparing two techniques of association rules as the FP-Growth algorithm and Apriori. According to this study, it was found that the application of FP-Growth algorithm was more suitable method for exploring the association rules of the shelves in the shop than Apriori. In addition, the results of the study revealed that the FP-Growth algorithm processed more quickly than Apriori as considered the running time to analysis layout of supermarket. The Apriori took average of time as 656.43 seconds and FP-Growth algorithm took average of time as 82.93 seconds that average of reduced time to 87.37%. Moreover, this study proposed an analysis of the efficiency improvement in disposing the shelves for the benefit of increasing business income through improving the shelves layout based on the association rules obtained from shopping path results of the study.