基于径向基函数神经网络的数据关联挖掘算法设计

Design of Data Association Mining Algorithms Based on Radial Basis Function Neural Network

为了提高用户需求数据挖掘的时效性及准确性,提出基于径向基函数神经网络的数据关联挖掘算法。在对数据频繁项集约束性关联规则研究的条件下,通过约简计算,得到满足约束条件的候选数据集,并对用户需求数据频繁项集更新;利用径向基函数神经网络对候选数据集进行训练优化,通过上界剪枝法,获取用户需求数据,并实现其实时挖掘。实验结果表明,所提数据挖掘算法执行效率受数据规模变化的影响较小,挖掘效率更高,扩展性更好。

In order to improve the timeliness and accuracy of user demand data mining,a data association mining algorithm based on radial basis function neural network is proposed.Under the condition of researching constrained association rules of frequent itemsets,candidate data sets satisfying constraints are obtained by reduction calculation,and frequent itemsets of user demand data are updated;candidate data sets are trained and optimized by radial basis function neural network,user demand data are obtained by upper bound pruning method,and real-time mining is realized.The experimental results show that the execution efficiency of the proposed data mining algorithm is less affected by the change of data size,and the mining efficiency is higher and the scalability is better.