Improving Efficiency of Apriori Algorithm Using Transaction Reduction

Association rules are the main technique to determine the frequent itemset in data mining. Apriori algorithm is a classical algorithm of association rule mining. This classical algorithm is inefficient due to so many scans of database. And if the database is large, it takes too much time to scan the database. In this paper, we proposed an Improved Apriori algorithm which reduces the scanning time by cutting down unnecessary transaction records as well as reduce the redundant generation of sub-items during pruning the candidate itemsets, which can form directly the set of frequent itemsets and eliminate candidate having a subset that is not frequent.