Association rule mining technique plays an important role in data mining research where the aim is to find interesting correlations between sets of items in databases. The apriori algorithm has been the most popular techniques in finding frequent patterns. However, when applying this method a database has to be scanned many times to calculate the counts of the huge umber of candidate items sets. A new algorithm has been proposed as a solution to this problem. The proposed algorithm is mainly concentrated to reduce the candidate sets generation and also aimed to increase the time of execution of the process.