Abstract
A large volume of transaction data is generated everyday in a number of applications. These dynamic data sets have immense potential for reflecting changes in customer behaviour patterns. One of the strategies of data mining is association rule discovery which correlates the occurrence of certain attributes in the database leading to the identification of large data itemsets. This paper seeks to generate large itemsets in a dynamic transaction database using the principles of Genetic Algorithms. Intra Transactions, Inter Transactions and Distributed Transactions are considered for mining Association Rules. Further, we analyze the time complexities of single scan technique DMARG (Dynamic Mining of Association Rules using Genetic Algorithms), with Fast UPdate (FUP) algorithm for intra transactions and E-Apriori for inter transactions. Our study shows that the algorithm DMARG outperforms both FUP and E-Apriori in terms of execution time and scalability, without compromising the quality or completeness of rules generated.