Association Rule Mining based on Apriori Algorithm in Minimizing Candidate Generation

Association Rule Mining is an area of data mining that focuses on pruning candidate keys. An Apriori algorithm is the most commonly used Association Rule Mining. This algorithm somehow has limitation and thus, giving the opportunity to do this research. This paper introduces a new way in which the Apriori algorithm can be improved. The modified algorithm introduces factors such as set size and set size frequency which in turn are being used to eliminate non significant candidate keys. With the use of these factors, the modified algorithm introduces a more efficient and effective way of minimizing candidate keys.