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Research on mobile internet big data detecting method for the redundant data

机译:移动互联网大数据冗余数据检测方法研究

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摘要

To address the low efficiency of the traditional cleaning method, this paper presents a build path tree clean method based on split method for identification of the redundant data, through the traditional mobile internet big data cleaning process for identifying the redundant data is analysed, by using median filtering algorithm, the features of redundant data are extracted. Redundant data is classified by support vector machine (SVM), and the redundant data is identified by self-organising feature map. Based on this, the redundant data identification model is built, which can clean the redundant data in mobile internet big data. Comparing with the classical methods, the simulation results show that the proposed method has the advantages of high accuracy, good stability, high recall rate, short time consuming and low energy consumption.
机译:针对传统清理方法效率低下的问题,提出了一种基于分割方法的构建路径树清理方法,用于冗余数据的识别,通过传统的移动互联网大数据清理过程,对冗余数据进行了分析。中值滤波算法,提取冗余数据的特征。冗余数据通过支持向量机(SVM)进行分类,冗余数据通过自组织特征图进行标识。在此基础上,建立了冗余数据识别模型,可以清除移动互联网大数据中的冗余数据。仿真结果表明,与传统方法相比,该方法具有精度高,稳定性好,召回率高,耗时短,能耗低的优点。

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