基于语义分割的异构多核平台大数据挖掘算法

Big Data Mining Algorithm of Heterogeneous Multi-core Platform Based on Semantic Segmentation

为了提高异构多核平台大数据精准挖掘能力,提出一种基于语义分割的异构多核平台大数据精准挖掘方法。构建异构多核平台大数据的模糊信息检测模型,采用关联特征提取方法进行异构多核平台大数据的模糊指向性聚类分析。构建异构多核平台大数据的输出自相关特征匹配模型,结合语义特征提取方法进行异构多核平台大数据的特征提取和统计分析。建立异构多核平台大数据的语义动态特征分析模型,提取异构多核平台大数据的统计特征量。根据异构多核平台大数据的特征提取结果采用模糊C均值聚类方法进行大数据聚类,采用语义分割进行异构多核平台大数据挖掘过程中的自适应寻优,实现异构多核平台大数据的优化挖掘。仿真结果表明,采用该方法进行异构多核平台大数据挖掘的精度较高,特征分辨能力较好,可提高异构多核平台大数据的挖掘和检测能力。

In order to improve the accurate mining ability of heterogeneous multi-core platform big data,a method based on semantic segmentation is proposed.The fuzzy information detection model of heterogeneous multi-core platform big data is constructed,and the fuzzy directional clustering analysis of heterogeneous multi-core platform big data is carried out by using association features extraction method.The output autocorrelation features matching model of heterogeneous multi-core platform big data is constructed,and the features extraction and statistical analysis of heterogeneous multi-core platform big data are carried out by semantic features extraction method.The semantic dynamic features analysis model of heterogeneous multi-core platform big data is established,and the statistical characteristics of heterogeneous multi-core platform big data are extracted.According to the features extraction results of heterogeneous multi-core platform big data,the fuzzy C-means clustering method is used for big data clustering,and the semantic segmentation is used for adaptive optimization in the process of heterogeneous multi-core platform big data mining to realize the optimized mining.The simulation results show that the proposed method has higher accuracy and better features resolution,which can improve the mining and detection ability of heterogeneous multi-core platform big data.