基于语义关联性特征融合的大数据挖掘方法

提出一种基于语义关联性特征融合的大数据挖掘算法.对云存储大数据分布式信息流进行高维相空间重构,在重构的相空间中提取大数据的语义关联维特征量,以提取的特征量为测试集进行自适应学习训练.采用模糊C均值算法进行大数据语义关联特征的稀疏性融合和聚类处理,在聚类中心实现对挖掘目标数据的指向性聚敛,输出数据挖掘结果,并采用特征压缩器进行降维处理,降低计算开销.仿真结果表明,采用该方法进行大数据挖掘的特征提取准确性较好,挖掘数据的聚类能力较强,在实时性和准确性方面具有优势.

Large Data Mining Method Based on Semantic Correlation Feature Fusion

A large data mining algorithm based on semantic correlation feature fusion is proposed.Phase space reconstruction of the cloud storage large distributed data flow is taken for information extraction,the semantic association feature is extracted in the reconstruction phase space,the extracted features are taken as the testing sets for the adaptive training.The fuzzy C means algorithm is taken for the big data semantic correlation feature sparsity fusion and the clustering processing,the directional clustering of mining target data is realized in the cluster center,the mining data is output,and the feature compressor is used to reduce the dimension and reduce computational overhead.Simulation results show that the method can mine the big data accurately,the clustering ability is stronger,and it has the advantages in real-time and accuracy.