Mining Frequent Closed Itemsets from Distributed Dataset

In this paper we address the problem of mining frequent closed itemsets in a highly distributed setting. The extraction of distributed frequent (close) itemsets is an important task in data mining. The paper shows how frequent closed itemsets, mined independently in each site, can be merged in order to derive globally frequent closed itemsets. Unfortunately, as distributed setting is various, it is unreasonable to adopt only one mining approach. The paper analyzes the distributed setting from three perspectives: 1 communication bandwidth; 2 site quantity; 3 the characteristic of each site dataset, and presents two mining approaches and their algorithms in their corresponding distributed setting. The experiment results indicate that our algorithms are efficient to their corresponding distributed setting.