云平台海量数据中提取用户信息数学建模仿真

发布时间：2018-04-17 00:31

  本文选题：云数据 + 海量数据　； 参考：《计算机仿真》2017年04期

【摘要】：云平台上海量数据中用户信息的提取,可更好地提升云提取的服务质量。对信息的准确提取,需要给出数据特征淘汰特性和过滤内在联系性,对数据特征进行匹配来完成。传统方法通过统计样本数据的频率表,提取每个数据特征的不一致性,但无法互相匹配,导致提取精度低。提出基于改进K近邻的云平台上海量数据中提取用户信息数学模型。以原始的云数据输人空间的特征为提取因子,对各个条件数据属性依据相同的权重提取特征样本间的距离,得到不同条件属性下相应特征参数的联合熵,给出数据特征淘汰特性和过滤的内在联系性,采用分数阶Fourier变换进行数据特征的匹配,构建了K-L数据特征分类器,以上述分类器为依据组建云平台上海量数据中提取用户信息数学模型。实验结果表明,所提模型提取精确度较高。
[Abstract]:The extraction of user information from cloud platform Shanghai quantity data can improve the service quality of cloud extraction.To extract the information accurately, we need to give the characteristic of data feature elimination and the inherent relation of filtering, and match the data feature to complete.The traditional method extracts the inconsistency of each data feature through the frequency table of statistical sample data, but it can not match each other, which leads to the low precision of extraction.A mathematical model of extracting user information from cloud platform Shanghai quantity data based on improved K nearest neighbor is proposed.With the feature of the original cloud data input into human space as the extraction factor, the joint entropy of the corresponding feature parameters under different condition attributes is obtained by extracting the distance between the feature samples for each conditional data attribute according to the same weight.The intrinsic relation between feature elimination and filtering is given. A K-L data feature classifier is constructed by using fractional order Fourier transform to match data features.Based on the above classifier, a mathematical model of extracting user information from cloud platform Shanghai quantity data is established.The experimental results show that the proposed model has high accuracy.
【作者单位】： 贵州财经大学数学与统计学院;
【分类号】：O141.4;TP393.09
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本文编号：1761289