Computing frequent itemsets in parallel using partial support trees

A key process in association rules mining, which has attracted a lot of interest during the last decade, is the discovery of frequent sets of items in a database of transactions. A number of sequential algorithms have been proposed that accomplish this task. On the other hand, only few parallel algorithms have appeared in the literature. In this paper, we study the parallelization of the partial-support-tree approach Goulbourne et al. (2000). Numerical results show that this method is generally competitive, while it is particularly adequate for certain types of datasets.