云存储中心多源文本主题融合模型研究
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摘要
为了提高云存储中心多源文本主题信息资源的开发能力和调度能力,提高云存储中心多源文本主题检索效率,提出一种基于关联规则挖掘的云存储中心多源文本主题融合模型。构建云存储中心多源文本主题分布大数据模型,采用相空间重构方法进行大数据的特征分布式重建和融合聚类处理,提取云存储中心多源文本主题信息的关联规则特征量,采用相关性检测技术进行信息集成滤波,结合模糊聚类方法进行云存储中心多源文本主题特征分类处理,根据分类结果实现信息融合。仿真结果表明,采用该方法进行云存储中心多源文本主题信息融合的特征分类性较好,提高了云存储中心进行数据检索的准确率,数据召回性和检索效率等性能指标表现较好。
In order to improve the development ability and scheduling ability of cloud storage center multi-source text topic information resource and improve the retrieval efficiency of cloud storage center multi-source text topic,a multi-source text topic fusion model based on association rule mining is proposed. The big data model of multi-source text topic distribution in cloud storage center is constructed,then the feature distributed reconstruction and fusion clustering processing are carried out by using phase space reconstruction method,and the association rules feature quantity of multi-source text topic information in cloud storage center is extracted. The correlation detection technique is used for information integration filtering and the fuzzy clustering method is used to classify multi-source text features in cloud storage center. The information fusion is realized based on the classification results. The simulation results show that this method has better feature classification for cloud storage center multi-source text subject information fusion,and improves the accuracy of data retrieval in cloud storage center,data recall and retrieval efficiency.
In order to improve the development ability and scheduling ability of cloud storage center multi-source text topic information resource and improve the retrieval efficiency of cloud storage center multi-source text topic,a multi-source text topic fusion model based on association rule mining is proposed. The big data model of multi-source text topic distribution in cloud storage center is constructed,then the feature distributed reconstruction and fusion clustering processing are carried out by using phase space reconstruction method,and the association rules feature quantity of multi-source text topic information in cloud storage center is extracted. The correlation detection technique is used for information integration filtering and the fuzzy clustering method is used to classify multi-source text features in cloud storage center. The information fusion is realized based on the classification results. The simulation results show that this method has better feature classification for cloud storage center multi-source text subject information fusion,and improves the accuracy of data retrieval in cloud storage center,data recall and retrieval efficiency.
引文
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[3]高悦,王文贤,杨淑贤.一种基于狄利克雷过程混合模型的文本聚类算法[J].信息网络安全,2015(11):60-65.
[4]易利容,王绍宇,殷丽丽,等.基于多变量LSTM的工业传感器时序数据预测[J].智能计算机与应用,2018,8(5):13-16.
[5]王伟,胡长武,郭栋,等.一种面向云构软件的云操作系统[J].计算机科学,2017,44(11):33-40.
[6]FERCOQ O,RICHTRIK P.Accelerated,parallel and proximal coordinate descent[J].SIAMJournal on Optimization,2015,25(4):1997-2023.
[7]刘测,韩家新.面向新闻文本的分类方法的比较研究[J].智能计算机与应用,2018,8(5):38-41.
[8]王树恒,吐尔根·依布拉音,卡哈尔江·阿比的热西提,等.基于BLSTM的维吾尔语文本情感分析[J].计算机工程与设计,2017,38(10):2879-2886.
[9]郑娜,王加阳.不完备序信息系统的证据特征及属性约简[J].计算机工程与应用,2018,54(21):43-47,200.
[10]李涛,王次臣,李华康.知识图谱的发展与构建[J].南京理工大学学报(自然科学版),2017,41(1):22-34.