IULFP: An efficient incremental updating algorithm based on LFP-tree for mining association rules

Dynamic rules acquisition is a topic of general interest in the field of association rules mining. Many existing incremental mining algorithms are Apriori-based, which are not easily adoptable to mine tree-based frequent patterns. In this paper, we provide a novel incremental updating algorithm IULFP for mining association rules. We use the layered frequent pattern tree based structure to store frequent items. Moreover, we propose the definition of “strong frequent itemsets”, which is proved to be a useful method to find all the frequent itemsets in the updated databases. The experimental results show that our approach has higher efficiency than other previous works.