Simulation Analysis of Network Difference Data Optimization Mining

GAO Jing; DONG Zhen-hua; GUO Feng

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Simulation Analysis of Network Difference Data Optimization Mining

更新时间：2026-09-04

- Simulation Analysis of Network Difference Data Optimization Mining
- Vol. 33, Issue 7, Pages: 136-139(2016)
- 作者机构：
  
  郑州成功财经学院机构信息工程系, 河南巩义,451200
- 作者简介：
- 基金信息：
- DOI：
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- Published：2016
- 稿件说明：
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高静, 董振华, 郭峰. 网络差异数据的优化挖掘模型仿真分析研究[J]. 微电子学与计算机, 2016, 33(7): 136-139.

GAO Jing, DONG Zhen-hua, GUO Feng. Simulation Analysis of Network Difference Data Optimization Mining[J]. 2016, 33(7): 136-139.
高静, 董振华, 郭峰. 网络差异数据的优化挖掘模型仿真分析研究[J]. 微电子学与计算机, 2016, 33(7): 136-139. DOI：

GAO Jing, DONG Zhen-hua, GUO Feng. Simulation Analysis of Network Difference Data Optimization Mining[J]. 2016, 33(7): 136-139. DOI：

摘要

提出一种基于反距离插值和分布式目标识别的网络差异数据优化挖掘算法.构建网络差异数据的采集和信号预处理模型

采用反距离插值扰动搜索聚类算法进行网络差异数据的分布源方位估计

将采样数据的协方差矩阵进行时间平均和空间平均

基于分布式目标识别算法实现对网络差异数据的准确挖掘.仿真结果表明

采用该算法能有效提高对网络差异数据的分布源估计的准确性

对数据的挖掘精度较高

性能优越于传统算法.

Abstract

A new data mining algorithm based on inverse distance interpolation and distributed object recognition is proposed. Building a network difference data acquisition and signal pre processing model

disturbance search clustering algorithm for network data difference distribution of the source range estimation using the inverse distance weighted interpolation

the sampling data covariance matrix to average the average time and space

based on distributed object recognition algorithm to achieve accurate mining difference data of network. Simulation results show that the proposed algorithm can effectively improve the accuracy of the distributed source estimation of network difference data

and it has higher accuracy and better performance than the traditional algorithm.

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references

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