The FP Growth algorithm is an advanced method for frequent pattern mining, primarily used in association rule mining. It serves as an improvement over the traditional Apriori algorithm, addressing its inefficiencies by significantly reducing the number of database scans required. Instead of generating candidate itemsets, FP Growth utilizes a compact data structure known as the FP-tree, which stores itemsets in a hierarchical format. This allows for faster processing and retrieval of frequent patterns. The algorithm is particularly effective in analyzing large datasets, making it a popular choice for applications such as market basket analysis and recommendation systems.