基于多变异粒子群优化算法的模糊关联规则挖掘

Mining Fuzzy Association Rules Based on Multi-mutation Particle Swarm Optimization Algorithm

针对事务数据库中连续型数值较难划分及粒子群优化算法易陷入局部最优的问题,提出一种用多变异粒子群优化算法进行模糊关联规则提取的框架,即先对连续型数值进行模糊区间划分,再通过多变异粒子群优化算法对划分结果进行模糊关联规则挖掘。分别对模糊划分方法和多变异粒子群优化算法的相关参数及框架等进行说明。在多组实验中进行比较分析,结果表明了该方法的准确性和有效性。

To deal with the problem that continuous value in the transaction database are difficult to divide and particle swarm optimization algorithm is easy to be troubled with local optimal, this paper proposed a framework about multi- mutation particle swarm optimization algorithm for extracting fuzzy association rules. Firstly, the continuous values are divided into the fuzzy interval. Then using multi-mutation particle swarm optimization algorithm to mine the fuzzy asso- ciation rules from the division results. This paper described the fuzzy division method and multi-mutation particle swarm optimization algorithm＇s parameters, framework and others. And it proved the accuracy and efficiency of this method by comparative analysis in several experiments.