关键词: 免疫优化, 遗传算法, 关联规则, 增量式挖掘

Abstract: Business activity and engineering practice always accumulate large dataset with important information.But because of the dataset’s largeness and frequent updating，if the Apriori based algorithm is applied to incremental association rules mining，it is not only inefficient，but also many redundant rules will be produced with low minimum support while some interesting rules will be lost with high minimum support，which leads to the algorithm’s blunt perception to those rules.So，following genetic principle，and combining with natural immune involution theory and relative bionic mechanism，this paper proposes an IOGA（Immune Optimization based Genetic Algorithm）approach for incremental association rules mining.Experiment demonstrates the proposed method’s effectiveness and presents its good performance in perceiving rules’ subtle change，reducing redundant rules and finding interesting rules.

Key words: immue optimization, genetic algorithm, association rules, incremental mining