Immune clone algorithm for mining association rules on dynamic databases

The paper seeks to generate large itemsets in a dynamic transaction database using immune clone algorithm. Intra transactions, inter transactions and distributed transactions are considered for mining association rules. The time of complexity of DMARICA (dynamic mining of association rules using immune clone algorithm) is analyzed, with fast updata (FUP) algorithm for intra transactions and e-apriori for inter transactions. The problem of mining association rules in the distributed environment is explored by distributed DMARICA (DDMARICA). The study shows that DMARICA outperforms both FUP and e-apriori in terms of execution time and scalability, without comprising the quality or completeness of rules generated. DMARICA is also compared with DMARG(dynamic mining of association rules using genetic algorithm). And it has better performance than that of DMARG