Research and improvement on Apriori algorithm of association rule mining

The classic Apriori algorithm for discovering frequent itemsets scans the database many times and the pattern matching between candidate itemsets and transactions is used repeatedly, so a large number of candidate itemsets were produced, which results in low efficiency of the algorithm. The improved Apriori algorithm improved it from three aspects: firstly, the strategy of the join step and the prune step was improved when candidate frequent (k+1)-itemsets were generated from frequent k-itemsets; secondly, the method of dealing with transaction was improved to reduce the time of pattern matching to be used in the Apriori algorithm; in the end, the method of dealing with database was improved, which lead to only once scanning of the database during the whole course of the algorithm. According to these improvements, an improved algorithm was introduced. The efficiency of Apriori algorithm got improvement both in time and in space. The experimental results of the improved algorithm show that the improved algorithm is more efficient than the original.