  Mining frequent itemsets from massive datasets is always being a most
important problem of data mining. Apriori is the most popular and simplest
algorithm for frequent itemset mining. To enhance the efficiency and
scalability of Apriori, a number of algorithms have been proposed addressing
the design of efficient data structures, minimizing database scan and parallel
and distributed processing. MapReduce is the emerging parallel and distributed
technology to process big datasets on Hadoop Cluster. To mine big datasets it
is essential to re-design the data mining algorithm on this new paradigm. In
this paper, we implement three variations of Apriori algorithm using data
structures hash tree, trie and hash table trie i.e. trie with hash technique on
MapReduce paradigm. We emphasize and investigate the significance of these
three data structures for Apriori algorithm on Hadoop cluster, which has not
been given attention yet. Experiments are carried out on both real life and
synthetic datasets which shows that hash table trie data structures performs
far better than trie and hash tree in terms of execution time. Moreover the
performance in case of hash tree becomes worst.
