摘要
为了提高大数据环境下关联数据挖掘的效率和精度,提出基于分数偏微积分分类数学模型的关联挖掘方法。基于偏微积分原理塑造基于偏微积分方程的融合算法模型,实现大数据分类过程中的差异性数据融合;再通过偏微分分类数学模型的双边界收敛控制,在数据集合融入偏微积分分类数据模型,通过增减量支持向量完成数据的模糊控制,采用约束捆绑聚类算法对数据模型实施挖掘,获取子序列,在最小迭代次数和收敛下,通过测度信息调控,采用高斯核函数挖掘关联数据序列。实验结果说明,所提关联数据挖掘方法具有较高的挖掘效率和精度,稳定性强。
An association mining method based on fractional partial calculus classification mathematical model is put forward to improve the efficiency and accuracy of association data mining under the environment of big data mining. On the basis of partial calculus principle,the fusion algorithm model based on partial calculus equations is constructed to realize the difference data fusion in the large data classification process. By means of the dual-boundary convergence control of partial differential classification mathematical model,the data set is integrated into the data model of partial calculus classification. The variation of support vector is used to realize the fuzzy control of data. The constraint bundling clustering algorithm is used to mine the data model to obtain the sub sequences. Under the conditions of minimum iteration times and convergence,the Gaussian kernel function is used to mine the association data sequence by means of measuring information control. The experimental results show that the proposed association data mining method has high mining efficiency and accuracy,and strong stability.
作者
温荣坤
WEN Rongkun(School of Engineering,Yunnan College of Business Management,Kunming 650300,China)
出处
《现代电子技术》
北大核心
2018年第13期95-99,共5页
Modern Electronics Technique
基金
云南省教育厅科学研究基金项目(2016ZDX273)~~
关键词
偏微积分分类
数学模型
关联挖掘
分数阶
收敛控制
挖掘效率
partial calculus classification；mathematical model；association mining；fractional order；convergence control；mining efficiency
