Parallel mining of maximal frequent itemsets from databases

In this paper, we propose a parallel algorithm for mining maximal frequent itemsets from databases. A frequent itemset is maximal if none of its supersets is frequent. The new parallel algorithm is named parallel max-miner (PMM), and it is a parallel version of the sequential max-miner algorithm by R.J. Bayardo (1998). Most of existing mining algorithms discover the frequent k-itemsets on the kth pass over the databases, and then generate the candidate (k + 1)-itemsets for the next pass. Compared to those level-wise algorithms, PMM looks ahead at each pass and prunes more candidate itemsets by checking the frequencies of their supersets. We implemented PMM on a cluster of workstations, and evaluated its performance for various cases. PMM demonstrated better performance than other sequential and parallel algorithms, and its performance is quite scalable, even when there are large maximal frequent itemsets (i.e. long patterns) in databases.