Distributed Mining of Frequent Closed Itemsets: Some Preliminary Results

In this paper we address the problem of mining frequent closed itemsets in a distributed setting. We figure out an environment where a transactional dataset is horizontally partitioned and stored in different sites. We assume that due to the huge size of datasets and privacy concerns dataset partitions cannot be moved to a centralized site where to materialize the whole dataset and perform the mining task. Thus it becomes mandatory to perform separate mining on each site, and then merge the local results do derive a global knowledge. This paper shows how frequent closed itemsets, mined independently in each site, can be merged in order to derive globally frequent closed itemsets. Unfortunately, such merging might produce a superset of all the frequent closed itemsets, while the associated supports could be smaller than the exact ones because some globally frequent closed itemsets might be not locally frequent in some partition. A post-processing phase is thus needed to compute exact global