融合遗传算法和关联规则的数据挖掘方法改进

提出了一种融合改进遗传算法(Genetic algorithm, GA)和关联规则的数据挖掘方法，首先将GA交叉算子和变异算子进行自适应改进，使其在迭代过程中能够根据函数适应度值自适应调节；然后将改进后的自适应GA融入到关联规则中，充分利用GA良好的全局搜索能力，提高处理海量数据关联规则的挖掘效率。为了避免无用规则，减少不相关性的存在，在此基础上融入亲密度以提高关联规则的可靠性。在Hadoop大数据平台上通过分析交通数据验证优化后的算法，与传统方法相比，该方法提高了算法的收敛速度和鲁棒性。

Improvement of Data Mining Method Combining Genetic Algorithm and Association Rules

A data mining method is presented by combining improved genetic algorithm (GA) and association rules. Firstly, the crossover operator and mutation operator of GA are improved adaptively so that they can adjust adaptively according to the fitness value of function in the process of iteration. The improved adaptive GA is integrated into association rules to make full use of the good global search ability of GA and improve the mining efficiency of association rules dealing with mass data. To avoid useless rules and reduce the existence of irrelevance, intimacy is added to improve the reliability of association rules. The optimized algorithm is verified by analyzing traffic data on Hadoop big data platform. Compared with traditional methods, this method improves the convergence speed and robustness of the algorithm.