Association rule mining is a data mining technique used to uncover previously unknown hidden patterns or rules from huge databases usually tera and peta bytes of data. There are many popular algorithms for mining various association rules like Apriori, portioning, dynamic item set counting etc. But the main drawback of these algorithms is their sequential nature. Processing large databases in sequential order has many disadvantages like time consuming, scalability and performance issues. In order to avoid the above said problems the authors look for parallel or distributed association rule mining for providing scalability and better performance.