作　　者： (张稼）; ;

机构地区： 广东工业大学华立学院

出　　处： 《电子测量技术》 2019年第4期79-83,共5页

摘　　要： 为了提高大型信息管理系统的数据检索和挖掘能力,提出了一种基于语义关联特征提取的大型信息管理系统数据挖掘技术。构建云存储模型进行大型信息管理系统中大数据分布式存储设计,结合大数据信息流的特征重组方法进行信息管理系统的优化结构重组,在重组的信息管理系统拓扑结构中提取信息管理分布数据的语义关联维特征量,以语义关联特征量为训练样本集进行信息管理系统的集成调度和数据挖掘,采用模糊C均值算法进行大型信息管理系统中分布数据语义关联特征的自适应融合和聚类处理,采用特征压缩器进行大型信息管理系统的存储空间降维处理,提高目标数据挖掘能力和信息管理系统的自适应调度能力。仿真结果表明,采用该方法进行大型信息管理系统数据挖掘的准确性较好,语义关联聚类性较强,提高了对信息管理系统目标数据的检索和调度能力。 In order to improve the ability of data retrieval and mining in large-scale information management system,a data mining technology of large scale information management system is proposed based on semantic association feature extraction.The cloud storage model is constructed to design the big data distributed storage in large information management system,and the optimized structure of the information management system is reorganized with the feature recombination method of big data information flow.The semantic association dimension feature quantity of the information management distribution data is extracted from the reorganized information management system topology,and the integrated scheduling and data mining of the information management system is carried out using the semantic association feature quantity as the training sample set.The fuzzy C-means algorithm is used for adaptive fusion and clustering of semantic association features of distributed data in large-scale information management system,and the feature compressor is used to reduce the dimension of storage space of large information management system.Improve the ability of target data mining and adaptive scheduling of information management system.The simulation results show that the method is accurate and semantic association clustering is strong,which improves the retrieval and scheduling ability of the target data in the information management system.

关 键 词： 语义 关联特征 信息管理系统 数据挖掘 信息检索

领　　域： [自动化与计算机技术—计算机软件与理论] [自动化与计算机技术—计算机科学与技术]