Frequent Item set Mining is a Data Mining task that has attracted the researchers’ interests in a way that very few other tasks have done. This concept is generally used in Decision Support problems. Many serial and parallel algorithms have been developed for Frequent Item set Mining. In this paper, we have focused on the developments of parallel algorithms in this area so far. We start with an Apriori-based parallel algorithm that focuses on minimizing the communication overhead even if, in parallel, it requires redundant duplicate computations. Then we discuss an algorithm that utilizes the system memory in amore effective manner. Then we give an account of the algorithm that reduces the synchronization between the processors, segments the database, and integrates load balancing. Then we describe an algorithm that partitions the Candidate Item sets intelligently. Finally, we give an account of an algorithm that combines two famous algorithms to leverage their cumulative advantage. While keeping the original ideas intact, we have avoided the cumbersome notations to keep it easily intelligible.