DSMFI-Miner: An Algorithm for Mining Maximal Frequent Itemsets on Data Streams

In order to improve the mining efficiency of frequent itemsets on data streams, we present an algorithm DSMFI_Miner for mining maximal frequent itemsets on data streams. First, a data structure DSMFI_tree is constructed to store the potential frequent itemsets and the data stream is divided into a set of segments, then the potential maximal frequent itemsets on each segment are obtained by a breadth-first algorithm, while the generated itemsets and their subsets are stored in the local DSMFI_tree which is updated dynamically, finally, the maximal frequent itemsets on the data stream can be rapidly found by a bottom-up search strategy from DSMFI_tree. The experimental result shows that the execution efficiency of DSMFI_Miner is better than that of INSTANT algorithm.