  Frequent Pattern Mining is a one field of the most significant topics in data
mining. In recent years, many algorithms have been proposed for mining frequent
itemsets. A new algorithm has been presented for mining frequent itemsets based
on N-list data structure called Prepost algorithm. The Prepost algorithm is
enhanced by implementing compact PPC-tree with the general tree. Prepost
algorithm can only find a frequent itemsets with required (pre-order and
post-order) for each node. In this chapter, we improved prepost algorithm based
on Hadoop platform (HPrepost), proposed using the Mapreduce programming model.
The main goals of proposed method are efficient mining frequent itemsets
requiring less running time and memory usage. We have conduct experiments for
the proposed scheme to compare with another algorithms. With dense datasets,
which have a large average length of transactions, HPrepost is more effective
than frequent itemsets algorithms in terms of execution time and memory usage
for all min-sup. Generally, our algorithm outperforms algorithms in terms of
runtime and memory usage with small thresholds and large datasets.
