Optimization of Execution Time using Association Rule Mining Algorithms

Abstract

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 (DFRDirect 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 itemsetsand 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. Now a day’s Hypermarket databases use data mining as a tool to optimize business solutions especially in the domain of sales and marketing. Common applications of data mining for any hypermarket include inventory management, tracking of customer behavior, finding frequent item sets and so on. The aim of this topic is to purpose efficiency analysis on data mining algorithms on aspects like Association Rule Mining. These aspects will help hypermarkets perform these functions effectively and hence increase their overall profit.