The FP-Growth algorithm is a powerful method used in data mining for frequent pattern mining, particularly in association rule mining. It serves as an improvement over the traditional Apriori algorithm by addressing its limitations, such as the need for multiple database scans and the generation of large candidate itemsets. FP-Growth utilizes a compact data structure known as the FP-tree, which allows it to efficiently store and traverse itemsets. By employing a divide-and-conquer strategy, FP-Growth significantly reduces computational costs and enhances performance, making it a preferred choice for analyzing large datasets and discovering meaningful patterns in transactional data.