Optimization of Execution Time Using Association Rule Mining Algorithms
The efficiency of mining association rules is an important field of Knowledge Discovery in Databases. The Apriori algorithm is a classical algorithm in mining association rules. This paper presents optimization of execution time for classicical apriori and an improved Apriori algorithm (DFR-Direct Fined and Remove) to increase the efficiency of generating association rules. This algorithm adopts a new method to reduce the redundant generation of sub-itemsets during pruning the candidate itemsets, which can form directly the set of frequent itemsets and eliminate candidates having a subset that is not frequent in the meantime. This algorithm can raise the probability of obtaining information in scanning database and reduce the potential scale of item sets.