Data mining technique for grouping products using clustering based on association

Abstract

There is high competition between these minimarkets so many products sold in each minimarket are not sold until they expire. The aim of this study is to help retail managers cluster products in minimarkets. The data obtained will be processed using the hybrid data mining approach by combining two methods in data mining. In the first section, association uses the FP-Growth algorithm, and in the second section, clustering uses the K-means algorithm. From the experimental results, it can be seen that the proposed approach can minimize the number of products to be grouped. After the association process is carried out, from 29 products in 12 transactions, 6 products can be obtained that has a frequency above the minimum support and minimum confidence. After the clustering process, 6 products are grouped into 2 clusters, so that 1 product is included in the most interested product cluster and 5 products are included in the interested product cluster. We minimize data processing so that retail managers can process data directly from sales transaction data on the cashier's computer and can quickly get the results of product grouping.