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Mining frequent itemsets from massive datasets is always being a mostimportant problem of data mining. Apriori is the most popular and simplestalgorithm for frequent itemset mining. To enhance the efficiency andscalability of Apriori, a number of algorithms have been proposed addressingthe design of efficient data structures, minimizing database scan and paralleland distributed processing. MapReduce is the emerging parallel and distributedtechnology to process big datasets on Hadoop Cluster. To mine big datasets itis essential to re-design the data mining algorithm on this new paradigm. Inthis paper, we implement three variations of Apriori algorithm using datastructures hash tree, trie and hash table trie i.e. trie with hash technique onMapReduce paradigm. We emphasize and investigate the significance of thesethree data structures for Apriori algorithm on Hadoop cluster, which has notbeen given attention yet. Experiments are carried out on both real life andsynthetic datasets which shows that hash table trie data structures performsfar better than trie and hash tree in terms of execution time. Moreover theperformance in case of hash tree becomes worst.

Performance Analysis of Apriori Algorithm with Different Data Structures on Hadoop Cluster