Improving Efficiency Apriori Algorithm by Reduction of candidate itemsets

Association Rules is an important field in Data Mining, which is used to discover useful knowledge from a massive databases. Association Rules have been used to extract the information from the database transactions, and Apriori Algorithm is a practical application for Association Rules and it is used to find frequent itemsets from database transactions. In this paper, we present a new improving on Apriori Algorithm by reduction generating of candidate itemsets and this leads to improving efficiency Apriori Algorithm.