Fast Updating Maximum Frequent Itemsets

Mining maximum frequent itemsets is a key problem in many data mining applications. In order to overcome the drawbacks of Apriori like algorithm for mining maximum frequent itemsets, DMFIA was proposed, which uses FP tree structure and the search strategy of top down, hence improves the efficiency for mining maximum frequent itemsets in some situations. But for given datasets with many frequent items and each maximum frequent itemset is not long, DMFIA need to search many levels and generate lots of maximum frequent candidate itemsets in each level. Therefore, this paper proposes IDMFIA (the improved algorithm of DMFIA) for mining maximum frequent itemsets, IDMFIA can prune the all supersets of mined maximum frequent itemset that contains a few items, and prune the all nonempty subsets of long maximum frequent itemset. Furthermore, this paper introduces FUMFIA(Fast Updating Maximum Frequent Itemsets Algorithm), which can efficiently use the created FP tree and the mined maximum frequent itemsets for updating the mined maximum frequent itemsets. Experimental results show that the two algorithms are effective and efficient.