ParallelCharMax: An Effective Maximal Frequent Itemset Mining Algorithm Based on MapReduce Framework

Abstract

Nowadays, the explosive growth in data collection in business and scientific areas has required the need to analyze and mine useful knowledge residing in these data. The recourse to data mining techniques seems to be inescapable in order to extract useful and novel patterns/models from large datasets. In this context, frequent itemsets (patterns) play an essential role in many data mining tasks that try to find interesting patterns from datasets. However, conventional approaches for mining frequent itemsets in Big Data era encounter significant challenges when computing power and memory space are limited. This paper proposes an efficient distributed frequent itemset mining algorithm, called ParallelCharMax, that is based on a powerful sequential algorithm, called Charm, and computes the maximal frequent itemsets that are considered perfect summaries of the frequent ones. The proposed algorithm has been implemented using MapReduce framework. The experimental component of the study shows the efficiency and the performance of the proposed algorithm compared with well known algorithms such as MineWithRounds and HMBA.