Implementation of Apriori and Fp-Growth Algorithms on Car Dealer Parts Sales with Association Rules

Abstract

Data mining is the process of discovering interesting and useful patterns and relationships in large volumes of data to produce valuable information. This information can help company leaders make decisions in various business areas. Company leaders can then develop strategies to face competition in the business world, one of which is the business of selling car parts. The availability of information systems related to car parts purchase transactions can be used to determine the association of parts stored in the database. The collection of transaction data can then be analyzed using the data mining process with the apriori and fp-growth algorithms using RapidMiner, which will produce information about a set of spare parts that are always purchased together more accurately, easily, and quickly. Using the information generated, management can use this information as one of the inputs in making strategic decisions in facing business competition, such as strategies for promotional needs, buyer segmentation, inventory stock, spare parts placement, or observing customer shopping patterns.