I. Introduction
Frequent item-set mining is one of the most important techniques to find out frequent item-sets in data mining [1]. Industries use the extracted frequent item-sets in decision making or setting policies. For example a retail-sector company is interested to know customer buying habits in particular area to sell out their product. Here, frequent item-set mining helps the company to know customer buying habits. On the other hand, even government of nations use the frequent item-set technique to extract useful information that further help to provide better services to people. Frequent item-set mining is the part of frequent pattern mining where frequent pattern represents those subsequences and sub-graphs which are occurred many times frequently in a given data sets. Traditional data mining tools fails to extract frequent item-sets when the size of transactional database is too large to compute. In Big Data era, we need a new approach to compute frequent item-sets where data-sets consist of millions of records [2]. Researchers proposed various approach to deal with Big Data challenges, but all these approaches suffers from synchronization, work load balancing and fault-tolerance problem [3]. To overcome this problem MapReduce model come into existence, originally proposed by Google [4]. This MapReduce model supports parallelization of tasks under Hadoop architecture [5].