Abstract: Data mining is a hot topic of research for past many years. Amongst the various algorithms popular and the researchers in progress in data mining Association rule mining (ARM) plays an important role due its tremendous publicity. It aims at extraction, discovery of hidden relation, exploration of interesting association between the existing items in a transactional database. It is used to generate frequent items and a set of rules to find frequent items. The entire purpose of this study is to highlight the fundamentals of association rule mining, compare the various modifications proposed to association rule mining approaches. The results generated by apriori algorithm can be further optimized using optimization algorithms. The study intends to determine the minimum support and minimum confidence values for mining association rules using the optimization algorithms. These algorithms are mainly defined for improving the performance of the Apriori algorithm. They minimize the quantization errors and fitness value to be improved. The algorithm improves the result produced by apriori algorithm. The major area of concentration is to optimize the rules generated by association rule mining (apriori method).
Keywords: Data mining ,Association RuleMining, Apriori Algorithm,Optimization Algorithms.