Research on Association Rule Mining Algorithm Based on FP-tree

In this era of data abundance, extracting valuable information from the vast ocean of data has become a challenge and a necessity. Especially in the field of association rule mining, uncovering hidden and meaningful relationships between items plays a crucial role not only in the retail industry but also in diverse fields such as healthcare and bioinformatics. The FP-tree, as an efficient tool for mining frequent patterns, demonstrates superior performance in handling large datasets compared to the traditional Apriori algorithm. This paper delves into the FP-tree-based association rule mining algorithm, starting from the principles of the FP-tree algorithm, introducing improved FP-tree design and construction methods, and validating the algorithm’s effectiveness through experiments. Through this research, the aim is to provide a more efficient and accurate method for mining association rules in handling large-scale datasets.