Institute of Electrical & Electronic Engineers
Extracting frequent itemsets is an important task in many data mining applications. When data are very large, it becomes mandatory to perform the mining task by using an external memory algorithm, but only a few of these algorithms have been proposed so far. Since also the result set of all the frequent itemsets is likely to be undesirably large, condensed representations, such as closed itemsets, have recently gained a lot of attention. In this paper, the authors discuss the limitations of the partitioning techniques adopted by external memory algorithms for extracting all the frequent itemsets, when applied to closed itemsets mining.