Abstract

The transaction database contains a very large and irregular dataset that requires another mechanism to read it, even though there is a lot of new knowledge that can be revealed, including associations or relationships between goods or products that are often purchased by customers. The new finding of the relationship between these variables is usually called association rule mining. The algorithm that is developing and often used is frequent pattern-growth (FP-Growth). The problem of very many transaction databases also occurred in Mr. A. So, in this research, we will look for customer patterns using the FP-Growth algorithm. The algorithm aims to find the maximum frequent itemset. The frequent itemset will be generated into associative rules so that it becomes valuable new knowledge. This knowledge can be used as a reference and consideration in making decisions. The FP-Growth algorithm will be implemented using the rapidminer tools on the transaction data of Mr.A's goods sales. The pattern of rules that will be searched for is based on data on sales of goods transactions. The results of the study obtained six association rules with five conclusions being the gift category. So that the suggestion for decision making is to lay out items close to and around the gift category in order to improve marketing and service strategies in order to attract the attention and interest of pointers in making purchases of goods.